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Model: NbAiLab/nb-wav2vec2-1b-nynorsk Source: Original Platform
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checkpoint-*/
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wandb/
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NbAiLab_NPSC_16K_mp3_nynorsk_test_eval_results.txt
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NbAiLab_NPSC_16K_mp3_nynorsk_test_eval_results.txt
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WER: 0.11319692134409612
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CER: 0.040263696587740365
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WER: 0.137413178149052
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CER: 0.04430940829451492
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README.md
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README.md
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---
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license: apache-2.0
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tags:
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- automatic-speech-recognition
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- NbAiLab/NPSC
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- no
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- nn
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- nb-NN
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datasets:
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- NbAiLab/NPSC
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language:
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- nn
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- no
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model-index:
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- name: nb-wav2vec2-1b-nynorsk
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: NPSC
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type: NbAiLab/NPSC
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args: 16K_mp3_nynorsk
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metrics:
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- name: Test (Nynorsk) WER
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type: wer
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value: 0.11319692134409612
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- name: Test (Nynorsk) CER
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type: cer
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value: 0.040263696587740365
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---
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# Norwegian Wav2Vec2 Model - 1B Nynorsk
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This model is finetuned on top of feature extractor [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-1b) from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
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- **WER: 0.1132** (0.1364)
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- **CER: 0.0402** (---)
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## Model description
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This is one of several Wav2Vec-models our team created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). This is the complete list of our models and their final scores:
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| Model | Final WER | |
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|:--------------|:------------|:------------:|
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| [NbAiLab/nb-wav2vec2-1b-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-bokmaal) | 6.33 | |
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| [NbAiLab/nb-wav2vec2-300m-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-bokmaal) | 7.03 | |
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| NbAiLab/nb-wav2vec2-1b-nynorsk (this model) | 11.32 | |
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| [NbAiLab/nb-wav2vec2-300m-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-nynorsk) | 12.22 | |
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## Dataset
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In parallel with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training.
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## Code
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We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.
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## Team
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The following people contributed to building this model: Rolv-Arild Braaten, Javier de la Rosa, and Freddy Wetjen.
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## Training procedure
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To reproduce these results, we strongly recommend that you follow the [instructions from 🤗](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#talks) to train a simple Swedish model.
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When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
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### Language Model
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As the scores indicate, adding even a simple 5-gram language will improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). You can also skip some of the steps in the guide, and copy the [5-gram model from this repo](https://huggingface.co/NbAiLab/XLSR-300M-bokmaal/tree/main/language_model).
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### Parameters
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The final model was run using these parameters:
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```
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--dataset_name="NbAiLab/NPSC"
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--model_name_or_path="facebook/wav2vec2-xls-r-1b"
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--dataset_config_name="16K_mp3_nynorsk"
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--output_dir="./"
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--overwrite_output_dir
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--num_train_epochs="40"
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--per_device_train_batch_size="12"
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--per_device_eval_batch_size="12"
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--gradient_accumulation_steps="2"
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--learning_rate="2e-5"
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--warmup_steps="2000"
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--length_column_name="input_length"
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--evaluation_strategy="steps"
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--text_column_name="text"
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--save_steps="500"
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--eval_steps="500"
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--logging_steps="100"
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--layerdrop="0.041"
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--attention_dropout="0.094"
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--activation_dropout="0.055"
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--hidden_dropout="0.047"
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--save_total_limit="3"
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--freeze_feature_encoder
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--feat_proj_dropout="0.04"
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--mask_time_prob="0.082"
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--mask_time_length="10"
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--mask_feature_prob="0.25"
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--mask_feature_length="64"
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--gradient_checkpointing
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--min_duration_in_seconds="0.5"
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--max_duration_in_seconds="30.0"
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--ctc_zero_infinity=True
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--use_auth_token
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--seed="42"
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--fp16
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--group_by_length
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--do_train --do_eval
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--push_to_hub
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--preprocessing_num_workers="16"
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```
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Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.
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| Parameter| Comment |
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|:-------------|:-----|
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| per_device_train_batch_size | Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system |
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|gradient_accumulation_steps |Can be adjusted even further up to increase batch size and speed up training without running into memory issues |
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| learning_rate|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability |
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| epochs| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs|
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## Citation
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```bibtex
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@inproceedings{de-la-rosa-etal-2023-boosting,
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title = "Boosting {N}orwegian Automatic Speech Recognition",
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author = "De La Rosa, Javier and
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Braaten, Rolv-Arild and
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Kummervold, Per and
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Wetjen, Freddy",
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booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
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month = may,
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year = "2023",
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address = "T{\'o}rshavn, Faroe Islands",
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publisher = "University of Tartu Library",
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url = "https://aclanthology.org/2023.nodalida-1.55",
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pages = "555--564",
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abstract = "In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokm{\aa}l and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10{\%} to 7.60{\%}, with models achieving 5.81{\%} for Bokm{\aa}l and 11.54{\%} for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.",
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}
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```
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See https://arxiv.org/abs/2307.01672
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133
README.old.md
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README.old.md
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---
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license: apache-2.0
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tags:
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- automatic-speech-recognition
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- NbAiLab/NPSC
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- generated_from_trainer
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model-index:
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- name: XLSR-1B-nynorsk-low
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: NPSC
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type: NbAiLab/NPSC
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args: 16K_mp3_nynorsk
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metrics:
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- name: Test (Nynorsk) WER
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type: wer
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value: 0.11319692134409612
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- name: Test (Nynorsk) CER
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type: cer
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value: 0.040263696587740365
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# XLSR-1B-nynorsk-low
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the NBAILAB/NPSC - 16K_MP3_NYNORSK dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2909
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- Wer: 0.1364
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 24
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- eval_batch_size: 24
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 2000
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- num_epochs: 60.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|
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| 2.8979 | 1.0 | 500 | 2.9413 | 1.0 |
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| 1.2224 | 2.0 | 1000 | 1.0359 | 0.7802 |
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| 0.8643 | 3.01 | 1500 | 0.7746 | 0.5969 |
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| 0.8211 | 4.01 | 2000 | 0.4882 | 0.3710 |
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| 0.5287 | 5.01 | 2500 | 0.4060 | 0.3085 |
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| 0.4724 | 6.01 | 3000 | 0.3297 | 0.2517 |
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| 0.4357 | 7.01 | 3500 | 0.3106 | 0.2342 |
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| 0.376 | 8.02 | 4000 | 0.2776 | 0.2072 |
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| 0.3286 | 9.02 | 4500 | 0.2888 | 0.2032 |
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| 0.3731 | 10.02 | 5000 | 0.2691 | 0.1835 |
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| 0.306 | 11.02 | 5500 | 0.2536 | 0.1835 |
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| 0.3025 | 12.02 | 6000 | 0.2758 | 0.1809 |
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| 0.3413 | 13.03 | 6500 | 0.2791 | 0.1823 |
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| 0.2601 | 14.03 | 7000 | 0.2912 | 0.1759 |
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| 0.2332 | 15.03 | 7500 | 0.2582 | 0.1694 |
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| 0.2108 | 16.03 | 8000 | 0.2717 | 0.1660 |
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| 0.2122 | 17.03 | 8500 | 0.2848 | 0.1647 |
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| 0.2369 | 18.04 | 9000 | 0.2548 | 0.1646 |
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| 0.1906 | 19.04 | 9500 | 0.2667 | 0.1627 |
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| 0.1943 | 20.04 | 10000 | 0.2662 | 0.1623 |
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| 0.18 | 21.04 | 10500 | 0.2769 | 0.1561 |
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| 0.1654 | 22.04 | 11000 | 0.2661 | 0.1558 |
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| 0.1515 | 23.05 | 11500 | 0.2870 | 0.1597 |
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| 0.147 | 24.05 | 12000 | 0.2778 | 0.1551 |
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| 0.1622 | 25.05 | 12500 | 0.2753 | 0.1541 |
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| 0.1522 | 26.05 | 13000 | 0.2932 | 0.1521 |
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| 0.1522 | 27.05 | 13500 | 0.2548 | 0.1513 |
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| 0.1319 | 28.06 | 14000 | 0.2811 | 0.1532 |
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| 0.1261 | 29.06 | 14500 | 0.2786 | 0.1521 |
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| 0.1391 | 30.06 | 15000 | 0.2651 | 0.1461 |
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| 0.1486 | 31.06 | 15500 | 0.2866 | 0.1494 |
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| 0.1121 | 32.06 | 16000 | 0.2641 | 0.1478 |
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| 0.1114 | 33.07 | 16500 | 0.2910 | 0.1478 |
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| 0.101 | 34.07 | 17000 | 0.2884 | 0.1443 |
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| 0.1135 | 35.07 | 17500 | 0.3029 | 0.1469 |
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| 0.0972 | 36.07 | 18000 | 0.2870 | 0.1467 |
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| 0.1178 | 37.07 | 18500 | 0.2745 | 0.1450 |
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| 0.0885 | 38.08 | 19000 | 0.2836 | 0.1440 |
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| 0.1144 | 39.08 | 19500 | 0.2761 | 0.1446 |
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| 0.0997 | 40.08 | 20000 | 0.2806 | 0.1439 |
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| 0.1012 | 41.08 | 20500 | 0.2878 | 0.1413 |
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| 0.0902 | 42.08 | 21000 | 0.2832 | 0.1452 |
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| 0.0804 | 43.09 | 21500 | 0.2911 | 0.1458 |
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| 0.0762 | 44.09 | 22000 | 0.2708 | 0.1441 |
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| 0.0758 | 45.09 | 22500 | 0.2804 | 0.1434 |
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| 0.0874 | 46.09 | 23000 | 0.2831 | 0.1407 |
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| 0.0895 | 47.09 | 23500 | 0.2913 | 0.1396 |
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| 0.0975 | 48.1 | 24000 | 0.2956 | 0.1411 |
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| 0.0758 | 49.1 | 24500 | 0.2920 | 0.1385 |
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| 0.0704 | 50.1 | 25000 | 0.2788 | 0.1383 |
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| 0.0707 | 51.1 | 25500 | 0.2822 | 0.1388 |
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| 0.0664 | 52.1 | 26000 | 0.2876 | 0.1371 |
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| 0.0692 | 53.11 | 26500 | 0.2815 | 0.1377 |
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| 0.0799 | 54.11 | 27000 | 0.2806 | 0.1363 |
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| 0.0611 | 55.11 | 27500 | 0.2878 | 0.1363 |
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| 0.0759 | 56.11 | 28000 | 0.2900 | 0.1365 |
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| 0.0801 | 57.11 | 28500 | 0.2881 | 0.1375 |
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| 0.0644 | 58.12 | 29000 | 0.2898 | 0.1362 |
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| 0.068 | 59.12 | 29500 | 0.2913 | 0.1369 |
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### Framework versions
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- Transformers 4.17.0.dev0
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- Pytorch 1.10.2+cu113
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- Datasets 2.0.0
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- Tokenizers 0.11.0
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34
add_kenlm.py
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import argparse
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from transformers import AutoProcessor
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from transformers import Wav2Vec2ProcessorWithLM
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from pyctcdecode import build_ctcdecoder
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def main(args):
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processor = AutoProcessor.from_pretrained(args.model_name_or_path)
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vocab_dict = processor.tokenizer.get_vocab()
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sorted_vocab_dict = {
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k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])
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}
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decoder = build_ctcdecoder(
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labels=list(sorted_vocab_dict.keys()),
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kenlm_model_path=args.kenlm_model_path,
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)
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processor_with_lm = Wav2Vec2ProcessorWithLM(
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feature_extractor=processor.feature_extractor,
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tokenizer=processor.tokenizer,
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decoder=decoder,
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)
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processor_with_lm.save_pretrained(args.model_name_or_path)
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print(f"Run: ~/bin/build_binary language_model/*.arpa language_model/5gram.bin -T $(pwd) && rm language_model/*.arpa")
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_name_or_path', default="./", help='Model name or path. Defaults to ./')
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parser.add_argument('--kenlm_model_path', required=True, help='Path to KenLM arpa file.')
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parse_args()
|
||||
main(args)
|
||||
1
added_tokens.json
Normal file
1
added_tokens.json
Normal file
@@ -0,0 +1 @@
|
||||
{"<s>": 32, "</s>": 33}
|
||||
14
all_results.json
Normal file
14
all_results.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"epoch": 60.0,
|
||||
"eval_loss": 0.2909022867679596,
|
||||
"eval_runtime": 76.6595,
|
||||
"eval_samples": 820,
|
||||
"eval_samples_per_second": 10.697,
|
||||
"eval_steps_per_second": 0.457,
|
||||
"eval_wer": 0.1364027127710383,
|
||||
"train_loss": 0.29242472694807237,
|
||||
"train_runtime": 84446.646,
|
||||
"train_samples": 11961,
|
||||
"train_samples_per_second": 8.498,
|
||||
"train_steps_per_second": 0.355
|
||||
}
|
||||
1
alphabet.json
Normal file
1
alphabet.json
Normal file
@@ -0,0 +1 @@
|
||||
{"labels": [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "\u00e5", "\u00e6", "\u00f8", "\u2047", "", "<s>", "</s>"], "is_bpe": false}
|
||||
690
cardinal_numbers.py
Normal file
690
cardinal_numbers.py
Normal file
@@ -0,0 +1,690 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf-8
|
||||
|
||||
"""From https://github.com/peresolb/number-conversion/"""
|
||||
import sys
|
||||
import os
|
||||
import nltk
|
||||
|
||||
|
||||
# Dict for basic primitive numbers: 1-10
|
||||
b = {
|
||||
1: "én",
|
||||
2: "to",
|
||||
3: "tre",
|
||||
4: "fire",
|
||||
5: "fem",
|
||||
6: "seks",
|
||||
7: "sju",
|
||||
8: "åtte",
|
||||
9: "ni",
|
||||
10: "ti",
|
||||
}
|
||||
b_nn = {
|
||||
1: "ein",
|
||||
2: "to",
|
||||
3: "tre",
|
||||
4: "fire",
|
||||
5: "fem",
|
||||
6: "seks",
|
||||
7: "sju",
|
||||
8: "åtte",
|
||||
9: "ni",
|
||||
10: "ti",
|
||||
}
|
||||
|
||||
# Dict for teen primitive numbers: 11-19
|
||||
t = {
|
||||
11: "elleve",
|
||||
12: "tolv",
|
||||
13: "tretten",
|
||||
14: "fjorten",
|
||||
15: "femten",
|
||||
16: "seksten",
|
||||
17: "sytten",
|
||||
18: "atten",
|
||||
19: "nitten",
|
||||
}
|
||||
|
||||
|
||||
# Dict for two digit primitive numbers: 20-90
|
||||
do = {
|
||||
20: "tjue",
|
||||
30: "tretti",
|
||||
40: "førti",
|
||||
50: "femti",
|
||||
60: "seksti",
|
||||
70: "sytti",
|
||||
80: "åtti",
|
||||
90: "nitti",
|
||||
}
|
||||
|
||||
|
||||
# Dict for the 3 digit primitive number: 100
|
||||
doo = {100: "hundre"}
|
||||
|
||||
|
||||
# Dict for the 4 digit primitive number: 1000
|
||||
dooo = {1000: "tusen"}
|
||||
|
||||
|
||||
# Reverser function for primitive number dicts
|
||||
def _revdict(numberdict):
|
||||
newdict = {}
|
||||
for k, v in numberdict.items():
|
||||
newdict[v] = k
|
||||
return newdict
|
||||
|
||||
|
||||
# The reverse of the primitive number dicts, where strings are keys and integers are values,
|
||||
# are name of original dict underscore 'r'
|
||||
b_r, b_nn_r, t_r, do_r, doo_r, dooo_r = (
|
||||
_revdict(b),
|
||||
_revdict(b_nn),
|
||||
_revdict(t),
|
||||
_revdict(do),
|
||||
_revdict(doo),
|
||||
_revdict(dooo),
|
||||
)
|
||||
|
||||
|
||||
def _oneten(nr, reverse=False, nn=False):
|
||||
"""Function taking an int from 1-10 and returning the corresponding word,
|
||||
or, if reverse=True, taking a numberword from 1-10 and returning the digit"""
|
||||
if reverse == False:
|
||||
if not type(nr) is int:
|
||||
return None
|
||||
if nr <= 10:
|
||||
if nn == False:
|
||||
return b[nr]
|
||||
else:
|
||||
return b_nn[nr]
|
||||
else:
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if nn == False:
|
||||
if nr in b_r.keys():
|
||||
return b_r[nr]
|
||||
else:
|
||||
if nr in b_nn_r.keys():
|
||||
return b_nn_r[nr]
|
||||
|
||||
|
||||
def _onedig(nr, reverse=False, nn=False):
|
||||
if reverse == False:
|
||||
if not _oneten(nr) == "ti":
|
||||
if nn == False:
|
||||
return _oneten(nr)
|
||||
else:
|
||||
return _oneten(nr, nn=True)
|
||||
if reverse == True:
|
||||
if not _oneten(nr, reverse=True) == 10:
|
||||
if nn == False:
|
||||
return _oneten(nr, reverse=True)
|
||||
else:
|
||||
return _oneten(nr, reverse=True, nn=True)
|
||||
|
||||
|
||||
def _teen(nr, reverse=False):
|
||||
"""Function taking a primitive two-digit int in the teen range and returning the
|
||||
corresponding word, or, if reverse=True, the corresponding number word"""
|
||||
if reverse == False:
|
||||
if not type(nr) is int:
|
||||
return None
|
||||
if nr in t.keys():
|
||||
return t[nr]
|
||||
else:
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if nr in t_r.keys():
|
||||
return t_r[nr]
|
||||
|
||||
|
||||
def _twodig(nr, reverse=False):
|
||||
"""Function taking a primitive two-digit int in the range 20-90 and returning
|
||||
the corresponding word, or, if reverse=True, the corresponding number word"""
|
||||
if reverse == False:
|
||||
if not type(nr) is int:
|
||||
return None
|
||||
if nr in do.keys():
|
||||
return do[nr]
|
||||
else:
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if nr in do_r.keys():
|
||||
return do_r[nr]
|
||||
|
||||
|
||||
def _numparser(numword, nn=False):
|
||||
"""Parse word to see if they start with a wd in firstnumwords.
|
||||
If yes, return firstnumword and second part"""
|
||||
if not type(numword) is str:
|
||||
return None
|
||||
firstpart = ""
|
||||
scndpart = ""
|
||||
firstnumwords = list(do_r.keys())
|
||||
for s in firstnumwords:
|
||||
if numword.startswith(s):
|
||||
slength = len(s)
|
||||
firstpart = s
|
||||
scndpart = numword[slength:]
|
||||
if nn == False:
|
||||
if (
|
||||
scndpart in b_r.keys() and b_r[scndpart] < 10
|
||||
): # Only return if second part is dig below 10
|
||||
return (firstpart, scndpart)
|
||||
else:
|
||||
if (
|
||||
scndpart in b_nn_r.keys() and b_nn_r[scndpart] < 10
|
||||
): # Only return if second part is dig below 10
|
||||
return (firstpart, scndpart)
|
||||
|
||||
|
||||
def _one_to_nineteen(nr, reverse=False, nn=False):
|
||||
"""Function taking a primitive two-digit int in the range 1-19
|
||||
and returning the corresponding word, or, if reverse=True, the corresponding number word"""
|
||||
if reverse == False:
|
||||
if not type(nr) is int:
|
||||
return None
|
||||
if nr < 11:
|
||||
if nn == False:
|
||||
return _oneten(nr)
|
||||
else:
|
||||
return _oneten(nr, nn=True)
|
||||
elif nr < 20:
|
||||
return _teen(nr)
|
||||
else:
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if nn == False:
|
||||
if type(_oneten(nr, reverse=True)) is int:
|
||||
return _oneten(nr, reverse=True)
|
||||
elif type(_teen(nr, reverse=True)):
|
||||
return _teen(nr, reverse=True)
|
||||
else:
|
||||
if type(_oneten(nr, reverse=True, nn=True)) is int:
|
||||
return _oneten(nr, reverse=True, nn=True)
|
||||
elif type(_teen(nr, reverse=True)):
|
||||
return _teen(nr, reverse=True)
|
||||
|
||||
|
||||
def _one_to_nn(nr, reverse=False, nn=False):
|
||||
"""Function taking an int in the range 1-99 and returning the corresponding word. Reverse as before"""
|
||||
if reverse == False:
|
||||
if not type(nr) is int:
|
||||
return None
|
||||
if nr > 0:
|
||||
if nr < 20:
|
||||
if nn == False:
|
||||
return _one_to_nineteen(nr)
|
||||
else:
|
||||
return _one_to_nineteen(nr, nn=True)
|
||||
elif nr < 100:
|
||||
if nr in do.keys():
|
||||
return _twodig(nr)
|
||||
else:
|
||||
nrstring = str(nr)
|
||||
frstdig = int(nrstring[0]) * 10
|
||||
scndig = int(nrstring[1])
|
||||
frstwd = _twodig(frstdig)
|
||||
if nn == False:
|
||||
scnwd = _onedig(scndig)
|
||||
else:
|
||||
scnwd = _onedig(scndig, nn=True)
|
||||
nrwd = frstwd + scnwd
|
||||
return nrwd
|
||||
else:
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if nn == False:
|
||||
if type(_one_to_nineteen(nr, reverse=True)) is int:
|
||||
return _one_to_nineteen(nr, reverse=True)
|
||||
elif type(_twodig(nr, reverse=True)) is int:
|
||||
return _twodig(nr, reverse=True)
|
||||
else:
|
||||
if _numparser(nr) == None:
|
||||
return None
|
||||
parsed = _numparser(nr)
|
||||
first = _twodig(parsed[0], reverse=True)
|
||||
second = _one_to_nineteen(parsed[1], reverse=True)
|
||||
return first + second
|
||||
else:
|
||||
if type(_one_to_nineteen(nr, reverse=True, nn=True)) is int:
|
||||
return _one_to_nineteen(nr, reverse=True, nn=True)
|
||||
elif type(_twodig(nr, reverse=True)) is int:
|
||||
return _twodig(nr, reverse=True)
|
||||
else:
|
||||
if _numparser(nr, nn=True) == None:
|
||||
return None
|
||||
parsed = _numparser(nr, nn=True)
|
||||
first = _twodig(parsed[0], reverse=True)
|
||||
second = _one_to_nineteen(parsed[1], reverse=True, nn=True)
|
||||
return first + second
|
||||
|
||||
|
||||
def _one_to_nnn(nr, reverse=False, nn=False):
|
||||
"""Function taking an int in the range 1-999 and returning the corresponding word. Reverse as before"""
|
||||
if reverse == False:
|
||||
if not type(nr) is int:
|
||||
return None
|
||||
if nr == 0:
|
||||
return None
|
||||
if nr < 100: # 1-99
|
||||
if nn == False:
|
||||
return _one_to_nn(nr)
|
||||
else:
|
||||
return _one_to_nn(nr, nn=True)
|
||||
elif nr < 1000:
|
||||
if nr == 100: # 100
|
||||
return doo[100]
|
||||
else:
|
||||
nrstring = str(nr) # 435 181
|
||||
frstdig = int(nrstring[0]) # 4 1
|
||||
scndig = int(nrstring[1]) # 3 8
|
||||
thrdig = int(nrstring[2]) # 5 1
|
||||
scthdig = int(nrstring[1:]) # 35 81
|
||||
if nn == False:
|
||||
frstwd = _onedig(frstdig) # fire
|
||||
else:
|
||||
frstwd = _onedig(frstdig, nn=True)
|
||||
nrwd = ""
|
||||
if scndig == 0: # 405 or 400
|
||||
if thrdig == 0: # 400
|
||||
nrwd = "%s %s" % (frstwd, doo[100]) # fire hundre
|
||||
else: # 405
|
||||
if nn == False:
|
||||
thrdwd = _one_to_nn(thrdig) # fem
|
||||
else:
|
||||
thrdwd = _one_to_nn(thrdig, nn=True)
|
||||
if frstdig != 1:
|
||||
nrwd = "%s %s og %s" % (
|
||||
frstwd,
|
||||
doo[100],
|
||||
thrdwd,
|
||||
) # fire hundre og fem
|
||||
else:
|
||||
nrwd = "%s og %s" % (doo[100], thrdwd) # hundre og fem
|
||||
else: # 435
|
||||
scthwd = ""
|
||||
if nn == False:
|
||||
scthwd = _one_to_nn(scthdig) # trettifem
|
||||
else:
|
||||
scthwd = _one_to_nn(scthdig, nn=True)
|
||||
if frstdig != 1:
|
||||
nrwd = "%s %s og %s" % (frstwd, doo[100], scthwd)
|
||||
else:
|
||||
nrwd = "%s og %s" % (doo[100], scthwd) # hundre og trettifem
|
||||
return nrwd
|
||||
else:
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if type(doo_r.get(nr, None)) is int: # hundre - 100
|
||||
return doo_r[nr]
|
||||
elif (
|
||||
len(nr.split(" ")) == 1
|
||||
and type(_one_to_nn(nr, reverse=True)) is int
|
||||
and nn == False
|
||||
):
|
||||
return _one_to_nn(nr, reverse=True) # 44
|
||||
elif (
|
||||
len(nr.split(" ")) == 1
|
||||
and type(_one_to_nn(nr, reverse=True, nn=True)) is int
|
||||
and nn == True
|
||||
):
|
||||
return _one_to_nn(nr, reverse=True, nn=True) # 44
|
||||
elif len(nr.split(" ")) == 2: # to hundre
|
||||
splitwords = nr.split(" ")
|
||||
if nn == False and nr == "ett hundre":
|
||||
return 100
|
||||
elif nn == True and nr == "eitt hundre":
|
||||
return 100
|
||||
elif (
|
||||
type(_one_to_nn(splitwords[0], reverse=True)) is int
|
||||
and splitwords[1] == "hundre"
|
||||
):
|
||||
return _one_to_nn(splitwords[0], reverse=True) * 100
|
||||
elif len(nr.split(" ")) == 3: # hundre og tre
|
||||
splitwords = nr.split(" ")
|
||||
if splitwords[0] == "hundre" and splitwords[1] == "og":
|
||||
if nn == False:
|
||||
if type(_one_to_nn(splitwords[2], reverse=True)) is int:
|
||||
return 100 + _one_to_nn(splitwords[2], reverse=True)
|
||||
else:
|
||||
if type(_one_to_nn(splitwords[2], reverse=True, nn=True)) is int:
|
||||
return 100 + _one_to_nn(splitwords[2], reverse=True, nn=True)
|
||||
else:
|
||||
return None
|
||||
elif len(nr.split(" ")) == 4: # ett hundre og trettifire, fire hundre og åtte
|
||||
splitwords = nr.split(" ")
|
||||
if nn == False:
|
||||
if (
|
||||
splitwords[0] == "ett"
|
||||
and splitwords[1] == "hundre"
|
||||
and splitwords[2] == "og"
|
||||
and type(_one_to_nn(splitwords[3], reverse=True)) is int
|
||||
): # ett hundre og trettifire
|
||||
return 100 + _one_to_nn(splitwords[3], reverse=True)
|
||||
elif (
|
||||
type(_one_to_nn(splitwords[0], reverse=True)) is int
|
||||
and _one_to_nn(splitwords[0], reverse=True) < 10
|
||||
and splitwords[1] == "hundre"
|
||||
and splitwords[2] == "og"
|
||||
and type(_one_to_nn(splitwords[3], reverse=True)) is int
|
||||
): # fire hundre og trettifire
|
||||
hundreds = _one_to_nn(splitwords[0], reverse=True) * 100
|
||||
tens = _one_to_nn(splitwords[3], reverse=True)
|
||||
return hundreds + tens
|
||||
else:
|
||||
return None
|
||||
else:
|
||||
if (
|
||||
splitwords[0] == "eitt"
|
||||
and splitwords[1] == "hundre"
|
||||
and splitwords[2] == "og"
|
||||
and type(_one_to_nn(splitwords[3], reverse=True, nn=True)) is int
|
||||
): # eit hundre og trettifire
|
||||
return 100 + _one_to_nn(splitwords[3], reverse=True, nn=True)
|
||||
elif (
|
||||
type(_one_to_nn(splitwords[0], reverse=True, nn=True)) is int
|
||||
and _one_to_nn(splitwords[0], reverse=True, nn=True) < 10
|
||||
and splitwords[1] == "hundre"
|
||||
and splitwords[2] == "og"
|
||||
and type(_one_to_nn(splitwords[3], reverse=True, nn=True)) is int
|
||||
): # fire hundre og trettifire
|
||||
hundreds = _one_to_nn(splitwords[0], reverse=True, nn=True) * 100
|
||||
tens = _one_to_nn(splitwords[3], reverse=True, nn=True)
|
||||
return hundreds + tens
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def _high_hundred(nr, nn=False):
|
||||
"""In Norwegian, as in English, it is possible to express the numbers 1100-1999 with hundreds,
|
||||
e.g. "tolv hundre og nittiåtte", /twelve hundred and ninety-eight/. We want to be able to convert
|
||||
these to integers. However, we don't need to produce them, so this algoritm only goes from strings to integers"""
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if len(nr.split(" ")) > 1:
|
||||
frstwd = nr.split(" ")[0]
|
||||
if not type(_teen(frstwd, reverse=True)) is int:
|
||||
return None
|
||||
frstdig = _teen(frstwd, reverse=True)
|
||||
if len(nr.split(" ")) == 2 and nr.split(" ")[1] == "hundre": # femten hundre
|
||||
return frstdig * 100
|
||||
elif (
|
||||
len(nr.split(" ")) == 4
|
||||
and nr.split(" ")[1] == "hundre"
|
||||
and nr.split(" ")[2] == "og"
|
||||
):
|
||||
if (
|
||||
nn == False and type(_one_to_nn(nr.split(" ")[3], reverse=True)) is int
|
||||
): # femten hundre og førtito
|
||||
lastdigs = _one_to_nn(nr.split(" ")[3], reverse=True)
|
||||
return (frstdig * 100) + lastdigs
|
||||
elif (
|
||||
nn == True
|
||||
and type(_one_to_nn(nr.split(" ")[3], reverse=True, nn=True)) is int
|
||||
): # femten hundre og førtito
|
||||
lastdigs = _one_to_nn(nr.split(" ")[3], reverse=True, nn=True)
|
||||
return (frstdig * 100) + lastdigs
|
||||
|
||||
|
||||
def _one_to_nnnnnn(nr, reverse=False, nn=False):
|
||||
"""Function taking an int in the range 1-999999 and returning the corresponding word. Reverse as before"""
|
||||
if reverse == False:
|
||||
if not type(nr) is int:
|
||||
return None
|
||||
if nr == 0:
|
||||
return None
|
||||
if nr < 1000: # 1-999
|
||||
if nn == False:
|
||||
return _one_to_nnn(nr)
|
||||
else:
|
||||
return _one_to_nnn(nr, nn=True)
|
||||
elif nr < 1000000: # 1000-999999
|
||||
if nr == 1000: # 1000
|
||||
if nn == False:
|
||||
return "ett tusen"
|
||||
else:
|
||||
return "eitt tusen"
|
||||
else:
|
||||
nrstring = str(nr) # Starting with last three digits. e.g. 23[456]
|
||||
ultdig = int(nrstring[-1]) # 6
|
||||
penultdig = int(nrstring[-2]) # 5
|
||||
antepenultdig = int(nrstring[-3]) # 4
|
||||
ult_and_penultdig = int(nrstring[-2:]) # 56
|
||||
ult_penult_antepenultdig = int(nrstring[-3:]) # 456
|
||||
tailstring = ""
|
||||
if antepenultdig == 0: # 012, 002, 000
|
||||
if penultdig == 0: # 000, 002
|
||||
if ultdig == 0: # 000
|
||||
tailstring = "tusen"
|
||||
else: # 002
|
||||
if nn == False:
|
||||
ultstring = _one_to_nnn(ultdig)
|
||||
tailstring = "tusen og %s" % ultstring # tusen og to
|
||||
else:
|
||||
ultstring = _one_to_nnn(ultdig, nn=True)
|
||||
tailstring = "tusen og %s" % ultstring # tusen og to
|
||||
else: # 012
|
||||
if nn == False:
|
||||
ult_and_penultstring = _one_to_nnn(ult_and_penultdig)
|
||||
tailstring = (
|
||||
"tusen og %s" % ult_and_penultstring
|
||||
) # tusen og tolv
|
||||
else:
|
||||
ult_and_penultstring = _one_to_nnn(
|
||||
ult_and_penultdig, nn=True
|
||||
)
|
||||
tailstring = (
|
||||
"tusen og %s" % ult_and_penultstring
|
||||
) # tusen og tolv
|
||||
else: # 456
|
||||
if nn == False:
|
||||
ult_penult_antepenultstring = _one_to_nnn(
|
||||
ult_penult_antepenultdig
|
||||
)
|
||||
if str(ult_penult_antepenultdig)[0] == "1":
|
||||
tailstring = (
|
||||
"tusen ett %s" % ult_penult_antepenultstring
|
||||
) # tusen ett hundre
|
||||
else:
|
||||
tailstring = (
|
||||
"tusen %s" % ult_penult_antepenultstring
|
||||
) # tusen fire hundre og femtiseks
|
||||
else:
|
||||
ult_penult_antepenultstring = _one_to_nnn(
|
||||
ult_penult_antepenultdig, nn=True
|
||||
)
|
||||
if str(ult_penult_antepenultdig)[0] == "1":
|
||||
tailstring = (
|
||||
"tusen eitt %s" % ult_penult_antepenultstring
|
||||
) # tusen ett hundre
|
||||
else:
|
||||
tailstring = (
|
||||
"tusen %s" % ult_penult_antepenultstring
|
||||
) # tusen fire hundre og femtiseks
|
||||
startdigs = int(
|
||||
nrstring[:-3]
|
||||
) # startstring can consist of the 1, 2 or 3 first digits
|
||||
startstring = ""
|
||||
if startdigs == 1: # 1001 starts with "ett"
|
||||
if nn == False:
|
||||
startstring = "ett"
|
||||
else:
|
||||
startstring = "eitt"
|
||||
elif startdigs > 99 and startdigs < 200: # 155555 starts with ett
|
||||
if nn == False:
|
||||
startnumstring = _one_to_nnn(startdigs)
|
||||
startstring = "ett %s" % startnumstring
|
||||
else:
|
||||
startnumstring = _one_to_nnn(startdigs, nn=True)
|
||||
startstring = "eitt %s" % startnumstring
|
||||
else: # the remaining numbers are purely compositional
|
||||
if nn == False:
|
||||
startstring = _one_to_nnn(startdigs)
|
||||
else:
|
||||
startstring = _one_to_nnn(startdigs, nn=True)
|
||||
numstring = "%s %s" % (startstring, tailstring)
|
||||
return numstring
|
||||
else:
|
||||
if not type(nr) is str:
|
||||
return None
|
||||
if type(_one_to_nnn(nr, reverse=True)) is int and nn == False:
|
||||
return _one_to_nnn(nr, reverse=True) # 444
|
||||
elif type(_one_to_nnn(nr, reverse=True, nn=True)) is int and nn == True:
|
||||
return _one_to_nnn(nr, reverse=True, nn=True) # 444
|
||||
elif nr == "tusen": # tusen - 1000
|
||||
return 1000
|
||||
elif (
|
||||
len(nr.split(" ")) > 1 and nr.split(" ")[-1] == "tusen"
|
||||
): # ett tusen, ett hundre tusen etc.
|
||||
wdlist = nr.split(" ")
|
||||
firstphrase = " ".join(wdlist[:-1])
|
||||
if type(_one_to_nnn(firstphrase, reverse=True)) is int and nn == False:
|
||||
firstdig = _one_to_nnn(firstphrase, reverse=True)
|
||||
return firstdig * 1000
|
||||
elif (
|
||||
type(_one_to_nnn(firstphrase, reverse=True, nn=True)) is int
|
||||
and nn == True
|
||||
):
|
||||
firstdig = _one_to_nnn(firstphrase, reverse=True, nn=True)
|
||||
return firstdig * 1000
|
||||
elif (
|
||||
len(nr.split(" ")) == 2 and nr.split(" ")[0] == "ett" and nn == False
|
||||
): # ett tusen
|
||||
return 1000
|
||||
elif len(nr.split(" ")) == 2 and nr.split(" ")[0] == "eitt" and nn == True:
|
||||
return 1000
|
||||
else: # misspellings should not result in return value
|
||||
return None
|
||||
else:
|
||||
if len(nr.split(" ")) > 1: # all other numbers should contain spaces
|
||||
numwordlist = nr.split(" ")
|
||||
if (
|
||||
"tusen" in numwordlist
|
||||
): # Find last part of numphrase, which starts with "tusen"
|
||||
tusenind = numwordlist.index("tusen") # find index of "tusen"
|
||||
lastwords = numwordlist[tusenind:] # words from 'tusen'
|
||||
firstwords = numwordlist[:tusenind] # words until 'tusen'
|
||||
lastdigs = 0
|
||||
if len(lastwords) == 3:
|
||||
if lastwords[1] == "og": # 'tusen og fire' 'tusen og førtifire'
|
||||
lastword = lastwords[-1]
|
||||
if nn == False:
|
||||
lastdigs = _one_to_nnn(lastword, reverse=True)
|
||||
elif nn == True:
|
||||
lastdigs = _one_to_nnn(lastword, reverse=True, nn=True)
|
||||
elif (
|
||||
nn == False
|
||||
and type(_one_to_nnn(" ".join(lastwords[1:]), reverse=True))
|
||||
is int
|
||||
and lastwords[2] == "hundre"
|
||||
): # tusen to hundre
|
||||
hundredphrase = " ".join(lastwords[1:])
|
||||
lastdigs = _one_to_nnn(hundredphrase, reverse=True)
|
||||
elif (
|
||||
nn == True
|
||||
and type(
|
||||
_one_to_nnn(
|
||||
" ".join(lastwords[1:]), reverse=True, nn=True
|
||||
)
|
||||
)
|
||||
is int
|
||||
and lastwords[2] == "hundre"
|
||||
): # tusen to hundre
|
||||
hundredphrase = " ".join(lastwords[1:])
|
||||
lastdigs = _one_to_nnn(hundredphrase, reverse=True, nn=True)
|
||||
else: # misspellings should not result in return value
|
||||
return None
|
||||
elif (
|
||||
len(lastwords) == 5 and lastwords[2] == "hundre"
|
||||
): # 'tusen fire hundre og fem'
|
||||
hundredphrase = " ".join(lastwords[1:])
|
||||
if nn == False:
|
||||
lastdigs = _one_to_nnn(hundredphrase, reverse=True)
|
||||
else:
|
||||
lastdigs = _one_to_nnn(hundredphrase, reverse=True, nn=True)
|
||||
else: # misspellings should not result in return value
|
||||
return None
|
||||
firstdigs = 0
|
||||
firstphrase = " ".join(firstwords)
|
||||
if len(firstwords) == 0: # as in 'tusen og tretti'
|
||||
firstdigs = 1000
|
||||
elif (
|
||||
len(firstwords) == 1 and firstwords[0] == "ett" and nn == False
|
||||
):
|
||||
firstdigs = 1000
|
||||
elif (
|
||||
len(firstwords) == 1 and firstwords[0] == "eitt" and nn == True
|
||||
):
|
||||
firstdigs = 1000
|
||||
elif (
|
||||
type(_one_to_nnn(firstphrase, reverse=True)) is int
|
||||
and nn == False
|
||||
):
|
||||
firstdigs = _one_to_nnn(firstphrase, reverse=True) * 1000
|
||||
elif (
|
||||
type(_one_to_nnn(firstphrase, reverse=True, nn=True)) is int
|
||||
and nn == True
|
||||
):
|
||||
firstdigs = (
|
||||
_one_to_nnn(firstphrase, reverse=True, nn=True) * 1000
|
||||
)
|
||||
else: # misspellings should not result in return value
|
||||
return None
|
||||
if type(firstdigs) is int and type(lastdigs) is int:
|
||||
return firstdigs + lastdigs
|
||||
|
||||
|
||||
def convert_nums(nr, reverse=False, nn=False):
|
||||
"""Functions for converting numbers. Only works for numbers in range 0-999999 for now"""
|
||||
if reverse == False:
|
||||
if type(nr) is int:
|
||||
returnstring = ""
|
||||
if nr == 0:
|
||||
returnstring = "null"
|
||||
elif nr < 1000000:
|
||||
if nn == False:
|
||||
returnstring = _one_to_nnnnnn(nr)
|
||||
else:
|
||||
returnstring = _one_to_nnnnnn(nr, nn=True)
|
||||
else:
|
||||
return None
|
||||
return returnstring
|
||||
else:
|
||||
if type(nr) is str:
|
||||
returnint = 0
|
||||
if nr == "null":
|
||||
returnint = 0
|
||||
elif nn == False and type(_one_to_nnnnnn(nr, reverse=True)) is int:
|
||||
returnint = _one_to_nnnnnn(nr, reverse=True)
|
||||
elif nn == True and type(_one_to_nnnnnn(nr, reverse=True, nn=True)) is int:
|
||||
returnint = _one_to_nnnnnn(nr, reverse=True, nn=True)
|
||||
elif nn == False and type(_high_hundred(nr)) is int:
|
||||
returnint = _high_hundred(nr)
|
||||
elif nn == True and type(_high_hundred(nr, nn=True)) is int:
|
||||
returnint = _high_hundred(nr, nn=True)
|
||||
else:
|
||||
return None
|
||||
return returnint
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# testing
|
||||
mydigit = 243564
|
||||
mynumstring = "hundre og atten tusen fire hundre og trettién"
|
||||
mydigit_nn = 34381
|
||||
mynumstring_nn = "tre hundre og førtiein"
|
||||
|
||||
print(
|
||||
"Digit conversion bm: %s\nString conversion bm: %s"
|
||||
% (convert_nums(mydigit), convert_nums(mynumstring, reverse=True))
|
||||
)
|
||||
print(
|
||||
"Digit conversion nn: %s\nString conversion nn: %s"
|
||||
% (
|
||||
convert_nums(mydigit_nn, nn=True),
|
||||
convert_nums(mynumstring_nn, nn=True, reverse=True),
|
||||
)
|
||||
)
|
||||
107
config.json
Normal file
107
config.json
Normal file
@@ -0,0 +1,107 @@
|
||||
{
|
||||
"_name_or_path": "facebook/wav2vec2-xls-r-1b",
|
||||
"activation_dropout": 0.055,
|
||||
"adapter_kernel_size": 3,
|
||||
"adapter_stride": 2,
|
||||
"add_adapter": false,
|
||||
"apply_spec_augment": true,
|
||||
"architectures": [
|
||||
"Wav2Vec2ForCTC"
|
||||
],
|
||||
"attention_dropout": 0.094,
|
||||
"bos_token_id": 1,
|
||||
"classifier_proj_size": 256,
|
||||
"codevector_dim": 1024,
|
||||
"contrastive_logits_temperature": 0.1,
|
||||
"conv_bias": true,
|
||||
"conv_dim": [
|
||||
512,
|
||||
512,
|
||||
512,
|
||||
512,
|
||||
512,
|
||||
512,
|
||||
512
|
||||
],
|
||||
"conv_kernel": [
|
||||
10,
|
||||
3,
|
||||
3,
|
||||
3,
|
||||
3,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"conv_stride": [
|
||||
5,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2,
|
||||
2
|
||||
],
|
||||
"ctc_loss_reduction": "mean",
|
||||
"ctc_zero_infinity": true,
|
||||
"diversity_loss_weight": 0.1,
|
||||
"do_stable_layer_norm": true,
|
||||
"eos_token_id": 2,
|
||||
"feat_extract_activation": "gelu",
|
||||
"feat_extract_dropout": 0.0,
|
||||
"feat_extract_norm": "layer",
|
||||
"feat_proj_dropout": 0.04,
|
||||
"feat_quantizer_dropout": 0.0,
|
||||
"final_dropout": 0.0,
|
||||
"hidden_act": "gelu",
|
||||
"hidden_dropout": 0.047,
|
||||
"hidden_size": 1280,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 5120,
|
||||
"layer_norm_eps": 1e-05,
|
||||
"layerdrop": 0.041,
|
||||
"mask_feature_length": 64,
|
||||
"mask_feature_min_masks": 0,
|
||||
"mask_feature_prob": 0.25,
|
||||
"mask_time_length": 10,
|
||||
"mask_time_min_masks": 2,
|
||||
"mask_time_prob": 0.082,
|
||||
"model_type": "wav2vec2",
|
||||
"num_adapter_layers": 3,
|
||||
"num_attention_heads": 16,
|
||||
"num_codevector_groups": 2,
|
||||
"num_codevectors_per_group": 320,
|
||||
"num_conv_pos_embedding_groups": 16,
|
||||
"num_conv_pos_embeddings": 128,
|
||||
"num_feat_extract_layers": 7,
|
||||
"num_hidden_layers": 48,
|
||||
"num_negatives": 100,
|
||||
"output_hidden_size": 1280,
|
||||
"pad_token_id": 31,
|
||||
"proj_codevector_dim": 1024,
|
||||
"tdnn_dilation": [
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
1,
|
||||
1
|
||||
],
|
||||
"tdnn_dim": [
|
||||
512,
|
||||
512,
|
||||
512,
|
||||
512,
|
||||
1500
|
||||
],
|
||||
"tdnn_kernel": [
|
||||
5,
|
||||
3,
|
||||
3,
|
||||
1,
|
||||
1
|
||||
],
|
||||
"torch_dtype": "float32",
|
||||
"transformers_version": "4.17.0.dev0",
|
||||
"use_weighted_layer_sum": false,
|
||||
"vocab_size": 34,
|
||||
"xvector_output_dim": 512
|
||||
}
|
||||
255
eval.py
Normal file
255
eval.py
Normal file
@@ -0,0 +1,255 @@
|
||||
#!/usr/bin/env python3
|
||||
import argparse
|
||||
import re
|
||||
from typing import Dict
|
||||
|
||||
import torch
|
||||
from datasets import Audio, Dataset, load_dataset, load_metric
|
||||
from num2words import num2words as n2w
|
||||
from slugify import slugify
|
||||
|
||||
from transformers import AutoFeatureExtractor, AutoModelForCTC, pipeline, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2FeatureExtractor
|
||||
# from pyctcdecode import BeamSearchDecoderCTC
|
||||
|
||||
from cardinal_numbers import convert_nums
|
||||
|
||||
|
||||
def log_results(result: Dataset, args: Dict[str, str]):
|
||||
"""DO NOT CHANGE. This function computes and logs the result metrics."""
|
||||
|
||||
log_outputs = args.log_outputs
|
||||
lm = "withLM" if args.use_lm else "noLM"
|
||||
model_id = args.model_id.replace("/", "_").replace(".", "")
|
||||
if args.filter:
|
||||
extra_args = [args.config, slugify(args.filter), args.split, lm]
|
||||
else:
|
||||
extra_args = [args.config, args.split, lm]
|
||||
dataset_id = "_".join([model_id] + args.dataset.split("/") + extra_args)
|
||||
|
||||
# load metric
|
||||
wer = load_metric("wer")
|
||||
cer = load_metric("cer")
|
||||
|
||||
# compute metrics
|
||||
wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
|
||||
cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
|
||||
|
||||
# print & log results
|
||||
result_str = f"{dataset_id}\nWER: {wer_result}\nCER: {cer_result}"
|
||||
print(result_str)
|
||||
|
||||
with open(f"{dataset_id}_eval_results.txt", "w") as f:
|
||||
f.write(result_str)
|
||||
with open(f"{dataset_id}_eval_results.tsv", "w") as f:
|
||||
f.write("\t".join([args.model_id, args.dataset, args.config, args.filter, args.split, str(lm), str(wer_result), str(cer_result)]))
|
||||
|
||||
# log all results in text file. Possibly interesting for analysis
|
||||
if log_outputs is not None:
|
||||
pred_file = f"log_{dataset_id}_predictions.txt"
|
||||
target_file = f"log_{dataset_id}_targets.txt"
|
||||
|
||||
with open(pred_file, "w") as p, open(target_file, "w") as t:
|
||||
# mapping function to write output
|
||||
def write_to_file(batch, i):
|
||||
p.write(f"{i}" + "\n")
|
||||
p.write(batch["prediction"] + "\n")
|
||||
t.write(f"{i}" + "\n")
|
||||
t.write(batch["target"] + "\n")
|
||||
|
||||
result.map(write_to_file, with_indices=True)
|
||||
|
||||
|
||||
def normalize_text(original_text: str, dataset: str) -> str:
|
||||
"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
|
||||
|
||||
text = original_text.lower()
|
||||
if dataset.lower().endswith("fleurs"):
|
||||
replacements = (
|
||||
(r"\be\.kr", "etter kristus fødsel"),
|
||||
(r"\bf\.kr", "før kristi fødsel"),
|
||||
(r"\bca[.]?\b", "circa"),
|
||||
(r"(\d)\s*km/t", r"\1 kilometer i timen"),
|
||||
(r"(\d)\s*km", r"\1 kilometer"),
|
||||
(r"(\d)\s*cm", r"\1 centimeter"),
|
||||
(r"(\d)\s*mm", r"\1 millimeter"),
|
||||
(r"kl\.", "klokka"),
|
||||
(r"f\.eks", "for eksempel"),
|
||||
)
|
||||
for abrev, expasion in replacements:
|
||||
text = re.sub(abrev, expasion, text)
|
||||
text = re.sub(r'(\d+)[-–](\d+)', r'\1 til \2', text) # 1-89, 70-90
|
||||
text = re.sub(r'(\d{2}):00', r'\1', text) # 21:00
|
||||
text = re.sub(r"(\d{2}):0(\d{1})", r"\1 null \2", text) # 17:03
|
||||
text = re.sub(r"(\d{1,2}):(\d{1,2})", r"\1 \2", text) # 17:23 (time), 4:3 (aspect ratios)
|
||||
text = re.sub(r"(1[1-9])00", r"\1 hundre", text) # 1800, 1900
|
||||
text = re.sub(r"(1[1-9])0([1-9])", r"\1 null \2 ", text) # 1901, 1909
|
||||
text = re.sub(r"(1[1-9])([1-9]\d)", r"\1 \2 ", text) # 1911, 1987
|
||||
text = re.sub(r"(20)0([1-9])", r"\1 null \2 ", text) # 2009
|
||||
text = re.sub(r"(20)(\d{2})", r"\1 \2 ", text) # 2009
|
||||
text = re.sub(r"(\d{1,3})[.](\d{1,2})", r"\1 dot \2 ", text) # 802.11n, 2.5ghz (in English)
|
||||
text = re.sub(r"(\d{1,2})[ .](\d{3})", r"\1\2", text) # 10 000, 32.000
|
||||
text = re.sub(r'(\w+)-(\w+)', r'\1 \2', text) # n-standard
|
||||
# text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: n2w(x.group(0), lang="no"), text.replace(".", ""))
|
||||
text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: convert_nums(int(x.group(0)), nn=True), text.replace(".", ""))
|
||||
|
||||
|
||||
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\<EFBFBD>\'\–\_\\\+\#\/]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
|
||||
text = re.sub(chars_to_ignore_regex, "", text) + " "
|
||||
|
||||
if dataset.lower().endswith("nst"):
|
||||
text = text.lower()
|
||||
text = text.replace("(...vær stille under dette opptaket...)", "")
|
||||
text = re.sub('[áàâ]', 'a', text)
|
||||
text = re.sub('[ä]', 'æ', text)
|
||||
text = re.sub('[éèëê]', 'e', text)
|
||||
text = re.sub('[íìïî]', 'i', text)
|
||||
text = re.sub('[óòöô]', 'o', text)
|
||||
text = re.sub('[ö]', 'ø', text)
|
||||
text = re.sub('[ç]', 'c', text)
|
||||
text = re.sub('[úùüû]', 'u', text)
|
||||
# text = re.sub('\\(?=(Punktum|Komma|Utropstegn|Spørsmålstegn))', ' ', text)
|
||||
text = re.sub('\s+', ' ', text)
|
||||
elif dataset.lower().endswith("npsc"):
|
||||
text = re.sub('[áàâ]', 'a', text)
|
||||
text = re.sub('[ä]', 'æ', text)
|
||||
text = re.sub('[éèëê]', 'e', text)
|
||||
text = re.sub('[íìïî]', 'i', text)
|
||||
text = re.sub('[óòöô]', 'o', text)
|
||||
text = re.sub('[ö]', 'ø', text)
|
||||
text = re.sub('[ç]', 'c', text)
|
||||
text = re.sub('[úùüû]', 'u', text)
|
||||
text = re.sub('\s+', ' ', text)
|
||||
elif dataset.lower().endswith("fleurs"):
|
||||
text = re.sub('[áàâ]', 'a', text)
|
||||
text = re.sub('[ä]', 'æ', text)
|
||||
text = re.sub('[éèëê]', 'e', text)
|
||||
text = re.sub('[íìïî]', 'i', text)
|
||||
text = re.sub('[óòöô]', 'o', text)
|
||||
text = re.sub('[ö]', 'ø', text)
|
||||
text = re.sub('[ç]', 'c', text)
|
||||
text = re.sub('[úùüû]', 'u', text)
|
||||
text = re.sub('[«»]', '', text)
|
||||
text = re.sub('\s+', ' ', text)
|
||||
text = re.sub('<e+h?>', 'eee', text)
|
||||
text = re.sub('<m+>', 'mmm', text)
|
||||
text = re.sub('<q+>', 'qqq', text)
|
||||
text = re.sub('<inaudible>', 'xxx', text)
|
||||
text = re.sub('[<>]', '', text)
|
||||
|
||||
# # In addition, we can normalize the target text, e.g. removing new lines characters etc...
|
||||
# # note that order is important here!
|
||||
# token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
|
||||
|
||||
# for t in token_sequences_to_ignore:
|
||||
# text = " ".join(text.split(t))
|
||||
|
||||
return text.strip()
|
||||
|
||||
|
||||
def main(args):
|
||||
# load dataset
|
||||
dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
|
||||
if args.filter:
|
||||
attribute, value = list(map(str.strip, args.filter.split(":")))
|
||||
dataset = dataset.filter(
|
||||
lambda x: x[attribute] == value,
|
||||
desc=f"Filtering on {args.filter}",
|
||||
)
|
||||
# for testing: only process the first two examples as a test
|
||||
# dataset = dataset.select(range(10))
|
||||
|
||||
# load processor
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
|
||||
sampling_rate = feature_extractor.sampling_rate
|
||||
|
||||
# resample audio
|
||||
dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
|
||||
|
||||
# load eval pipeline
|
||||
if args.device is None:
|
||||
args.device = 0 if torch.cuda.is_available() else -1
|
||||
# asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
|
||||
|
||||
model_instance = AutoModelForCTC.from_pretrained(args.model_id)
|
||||
if args.use_lm:
|
||||
processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
|
||||
decoder = processor.decoder
|
||||
else:
|
||||
processor = Wav2Vec2Processor.from_pretrained(args.model_id)
|
||||
decoder = None
|
||||
asr = pipeline(
|
||||
"automatic-speech-recognition",
|
||||
model=model_instance,
|
||||
tokenizer=processor.tokenizer,
|
||||
feature_extractor=processor.feature_extractor,
|
||||
decoder=decoder,
|
||||
device=args.device
|
||||
)
|
||||
|
||||
# feature_extractor_dict, _ = Wav2Vec2FeatureExtractor.get_feature_extractor_dict(args.model_id)
|
||||
# feature_extractor_dict["processor_class"] = "Wav2Vec2Processor" if not args.use_lm else "Wav2Vec2ProcessorWithLM"
|
||||
# feature_extractor = Wav2Vec2FeatureExtractor.from_dict(feature_extractor_dict)
|
||||
|
||||
# asr = pipeline("automatic-speech-recognition", model=args.model_id, feature_extractor=feature_extractor, device=args.device, decoder=BeamSearchDecoderCTC.load_from_dir("./"))
|
||||
|
||||
# map function to decode audio
|
||||
def map_to_pred(batch):
|
||||
prediction = asr(
|
||||
batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
|
||||
)
|
||||
|
||||
batch["prediction"] = prediction["text"]
|
||||
batch["target"] = normalize_text(batch[args.text_column], args.dataset)
|
||||
return batch
|
||||
|
||||
# run inference on all examples
|
||||
result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
|
||||
|
||||
# compute and log_results
|
||||
# do not change function below
|
||||
log_results(result, args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument(
|
||||
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--filter", type=str, default="", help="Simple filter on attributes. *E.g.* `region_of_youth:Troms` would pnly keep those samplesfor which the condition is met"
|
||||
)
|
||||
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
|
||||
parser.add_argument(
|
||||
"--text_column", type=str, default="text", help="Column name containing the transcription."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=int,
|
||||
default=None,
|
||||
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_lm", action="store_true", help="If defined, use included language model as the decoder."
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
main(args)
|
||||
9
eval_results.json
Normal file
9
eval_results.json
Normal file
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"epoch": 60.0,
|
||||
"eval_loss": 0.2909022867679596,
|
||||
"eval_runtime": 76.6595,
|
||||
"eval_samples": 820,
|
||||
"eval_samples_per_second": 10.697,
|
||||
"eval_steps_per_second": 0.457,
|
||||
"eval_wer": 0.1364027127710383
|
||||
}
|
||||
3
language_model/5gram.bin
Normal file
3
language_model/5gram.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ab572020f4871ec611ad95a92071040bafe77c4633de2d26166006749e964129
|
||||
size 3981756555
|
||||
1
language_model/attrs.json
Normal file
1
language_model/attrs.json
Normal file
@@ -0,0 +1 @@
|
||||
{"alpha": 0.5, "beta": 1.5, "unk_score_offset": -10.0, "score_boundary": true}
|
||||
3
language_model/unigrams.txt
Normal file
3
language_model/unigrams.txt
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:48aeb02d80e42421057e95161188779f1cdace619908c0feeab767d1cadb44cb
|
||||
size 14892613
|
||||
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_predictions.txt
Normal file
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_predictions.txt
Normal file
File diff suppressed because it is too large
Load Diff
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_predictions_no_lang_model.txt
Normal file
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_predictions_no_lang_model.txt
Normal file
File diff suppressed because it is too large
Load Diff
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_targets.txt
Normal file
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_targets.txt
Normal file
File diff suppressed because it is too large
Load Diff
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_targets_no_lang_model.txt
Normal file
1656
log_NbAiLab_NPSC_16K_mp3_nynorsk_test_targets_no_lang_model.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:55b79d680168317058eaa54710838e49501cba54bfc9403b6c9b83fa8c6c67cd
|
||||
size 3850265160
|
||||
10
preprocessor_config.json
Normal file
10
preprocessor_config.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"do_normalize": true,
|
||||
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
|
||||
"feature_size": 1,
|
||||
"padding_side": "right",
|
||||
"padding_value": 0,
|
||||
"processor_class": "Wav2Vec2ProcessorWithLM",
|
||||
"return_attention_mask": true,
|
||||
"sampling_rate": 16000
|
||||
}
|
||||
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5b5bc883abdd48ded6d3a22aa80034d5a314c0ae71cbc0ec5399909f7eac0a0d
|
||||
size 3850486961
|
||||
40
run.sh
Executable file
40
run.sh
Executable file
@@ -0,0 +1,40 @@
|
||||
WANDB_ENTITY=NbAiLab WANDB_PROJECT=wav2vec2 python run_speech_recognition_ctc.py \
|
||||
--dataset_name="NbAiLab/NPSC" \
|
||||
--model_name_or_path="facebook/wav2vec2-xls-r-1b" \
|
||||
--hub_model_id="NbAiLab/XLSR-1B-nynorsk-low" \
|
||||
--dataset_config_name="16K_mp3_nynorsk" \
|
||||
--output_dir="./" \
|
||||
--overwrite_output_dir \
|
||||
--num_train_epochs="60" \
|
||||
--per_device_train_batch_size="24" \
|
||||
--per_device_eval_batch_size="24" \
|
||||
--learning_rate="2e-5" \
|
||||
--warmup_steps="2000" \
|
||||
--length_column_name="input_length" \
|
||||
--evaluation_strategy="steps" \
|
||||
--text_column_name="text" \
|
||||
--save_steps="500" \
|
||||
--eval_steps="500" \
|
||||
--logging_steps="100" \
|
||||
--layerdrop="0.041" \
|
||||
--attention_dropout="0.094" \
|
||||
--activation_dropout="0.055" \
|
||||
--hidden_dropout="0.047" \
|
||||
--save_total_limit="3" \
|
||||
--freeze_feature_encoder \
|
||||
--feat_proj_dropout="0.04" \
|
||||
--mask_time_prob="0.082" \
|
||||
--mask_time_length="10" \
|
||||
--mask_feature_prob="0.25" \
|
||||
--mask_feature_length="64" \
|
||||
--gradient_checkpointing \
|
||||
--min_duration_in_seconds="0.5" \
|
||||
--max_duration_in_seconds="30.0" \
|
||||
--ctc_zero_infinity=True \
|
||||
--use_auth_token \
|
||||
--seed="42" \
|
||||
--fp16 \
|
||||
--group_by_length \
|
||||
--do_train --do_eval \
|
||||
--push_to_hub \
|
||||
--preprocessing_num_workers="16"
|
||||
41
run_recover.sh
Executable file
41
run_recover.sh
Executable file
@@ -0,0 +1,41 @@
|
||||
WANDB_ENTITY=NbAiLab WANDB_PROJECT=wav2vec2 python run_speech_recognition_ctc.py \
|
||||
--dataset_name="NbAiLab/NPSC" \
|
||||
--model_name_or_path="./" \
|
||||
--hub_model_id="NbAiLab/XLSR-1B-bokmaal-low" \
|
||||
--dataset_config_name="16K_mp3_bokmaal" \
|
||||
--output_dir="./" \
|
||||
--overwrite_output_dir \
|
||||
--num_train_epochs="34" \
|
||||
--per_device_train_batch_size="12" \
|
||||
--per_device_eval_batch_size="12" \
|
||||
--gradient_accumulation_steps="2" \
|
||||
--learning_rate="1.7e-5" \
|
||||
--warmup_steps="0" \
|
||||
--length_column_name="input_length" \
|
||||
--evaluation_strategy="steps" \
|
||||
--text_column_name="text" \
|
||||
--save_steps="500" \
|
||||
--eval_steps="500" \
|
||||
--logging_steps="100" \
|
||||
--layerdrop="0.041" \
|
||||
--attention_dropout="0.094" \
|
||||
--activation_dropout="0.055" \
|
||||
--hidden_dropout="0.047" \
|
||||
--save_total_limit="3" \
|
||||
--freeze_feature_encoder \
|
||||
--feat_proj_dropout="0.04" \
|
||||
--mask_time_prob="0.082" \
|
||||
--mask_time_length="10" \
|
||||
--mask_feature_prob="0.25" \
|
||||
--mask_feature_length="64" \
|
||||
--gradient_checkpointing \
|
||||
--min_duration_in_seconds="0.5" \
|
||||
--max_duration_in_seconds="30.0" \
|
||||
--ctc_zero_infinity=True \
|
||||
--use_auth_token \
|
||||
--seed="42" \
|
||||
--fp16 \
|
||||
--group_by_length \
|
||||
--do_train --do_eval \
|
||||
--push_to_hub \
|
||||
--preprocessing_num_workers="32"
|
||||
810
run_speech_recognition_ctc.py
Normal file
810
run_speech_recognition_ctc.py
Normal file
@@ -0,0 +1,810 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
|
||||
""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition"""
|
||||
|
||||
import functools
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
import warnings
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
import datasets
|
||||
import numpy as np
|
||||
import torch
|
||||
from datasets import DatasetDict, load_dataset, load_metric, concatenate_datasets
|
||||
|
||||
import transformers
|
||||
from transformers import (
|
||||
AutoConfig,
|
||||
AutoFeatureExtractor,
|
||||
AutoModelForCTC,
|
||||
AutoProcessor,
|
||||
AutoTokenizer,
|
||||
HfArgumentParser,
|
||||
Trainer,
|
||||
TrainingArguments,
|
||||
Wav2Vec2Processor,
|
||||
set_seed,
|
||||
)
|
||||
from transformers.trainer_utils import get_last_checkpoint, is_main_process
|
||||
from transformers.utils import check_min_version
|
||||
from transformers.utils.versions import require_version
|
||||
|
||||
|
||||
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
|
||||
check_min_version("4.16.0.dev0")
|
||||
|
||||
require_version("datasets>=1.13.3", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def list_field(default=None, metadata=None):
|
||||
return field(default_factory=lambda: default, metadata=metadata)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelArguments:
|
||||
"""
|
||||
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
||||
"""
|
||||
|
||||
model_name_or_path: str = field(
|
||||
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
||||
)
|
||||
tokenizer_name_or_path: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
|
||||
)
|
||||
cache_dir: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
|
||||
)
|
||||
freeze_feature_encoder: bool = field(
|
||||
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
|
||||
)
|
||||
attention_dropout: float = field(
|
||||
default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
|
||||
)
|
||||
activation_dropout: float = field(
|
||||
default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
|
||||
)
|
||||
feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
|
||||
hidden_dropout: float = field(
|
||||
default=0.0,
|
||||
metadata={
|
||||
"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
|
||||
},
|
||||
)
|
||||
final_dropout: float = field(
|
||||
default=0.0,
|
||||
metadata={"help": "The dropout probability for the final projection layer."},
|
||||
)
|
||||
mask_time_prob: float = field(
|
||||
default=0.05,
|
||||
metadata={
|
||||
"help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
|
||||
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
|
||||
"vectors will be masked along the time axis."
|
||||
},
|
||||
)
|
||||
mask_time_length: int = field(
|
||||
default=10,
|
||||
metadata={"help": "Length of vector span to mask along the time axis."},
|
||||
)
|
||||
mask_feature_prob: float = field(
|
||||
default=0.0,
|
||||
metadata={
|
||||
"help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
|
||||
"span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
|
||||
},
|
||||
)
|
||||
mask_feature_length: int = field(
|
||||
default=10,
|
||||
metadata={"help": "Length of vector span to mask along the feature axis."},
|
||||
)
|
||||
layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
|
||||
ctc_loss_reduction: Optional[str] = field(
|
||||
default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
|
||||
)
|
||||
ctc_zero_infinity: Optional[bool] = field(
|
||||
default=False, metadata={"help": "If True, will try yo aboud the CTC loss goinf to infinity."}
|
||||
)
|
||||
|
||||
@dataclass
|
||||
class DataTrainingArguments:
|
||||
"""
|
||||
Arguments pertaining to what data we are going to input our model for training and eval.
|
||||
|
||||
Using `HfArgumentParser` we can turn this class
|
||||
into argparse arguments to be able to specify them on
|
||||
the command line.
|
||||
"""
|
||||
|
||||
dataset_name: str = field(
|
||||
metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
||||
)
|
||||
dataset_config_name: str = field(
|
||||
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
||||
)
|
||||
train_split_name: str = field(
|
||||
default="train+validation",
|
||||
metadata={
|
||||
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
||||
},
|
||||
)
|
||||
eval_split_name: str = field(
|
||||
default="test",
|
||||
metadata={
|
||||
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
||||
},
|
||||
)
|
||||
audio_column_name: str = field(
|
||||
default="audio",
|
||||
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
|
||||
)
|
||||
text_column_name: str = field(
|
||||
default="text",
|
||||
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
|
||||
)
|
||||
overwrite_cache: bool = field(
|
||||
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
|
||||
)
|
||||
preprocessing_num_workers: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={"help": "The number of processes to use for the preprocessing."},
|
||||
)
|
||||
max_train_samples: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
||||
"value if set."
|
||||
},
|
||||
)
|
||||
max_eval_samples: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
|
||||
"value if set."
|
||||
},
|
||||
)
|
||||
chars_to_ignore: Optional[List[str]] = list_field(
|
||||
default=None,
|
||||
metadata={"help": "A list of characters to remove from the transcripts."},
|
||||
)
|
||||
eval_metrics: List[str] = list_field(
|
||||
default=["wer"],
|
||||
metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
|
||||
)
|
||||
max_duration_in_seconds: float = field(
|
||||
default=20.0,
|
||||
metadata={
|
||||
"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
|
||||
},
|
||||
)
|
||||
min_duration_in_seconds: float = field(
|
||||
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
|
||||
)
|
||||
preprocessing_only: bool = field(
|
||||
default=False,
|
||||
metadata={
|
||||
"help": "Whether to only do data preprocessing and skip training. "
|
||||
"This is especially useful when data preprocessing errors out in distributed training due to timeout. "
|
||||
"In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
|
||||
"so that the cached datasets can consequently be loaded in distributed training"
|
||||
},
|
||||
)
|
||||
use_auth_token: bool = field(
|
||||
default=False,
|
||||
metadata={
|
||||
"help": "If :obj:`True`, will use the token generated when running"
|
||||
":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
|
||||
},
|
||||
)
|
||||
unk_token: str = field(
|
||||
default="[UNK]",
|
||||
metadata={"help": "The unk token for the tokenizer"},
|
||||
)
|
||||
pad_token: str = field(
|
||||
default="[PAD]",
|
||||
metadata={"help": "The padding token for the tokenizer"},
|
||||
)
|
||||
word_delimiter_token: str = field(
|
||||
default="|",
|
||||
metadata={"help": "The word delimiter token for the tokenizer"},
|
||||
)
|
||||
phoneme_language: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The target language that should be used be"
|
||||
" passed to the tokenizer for tokenization. Note that"
|
||||
" this is only relevant if the model classifies the"
|
||||
" input audio to a sequence of phoneme sequences."
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DataCollatorCTCWithPadding:
|
||||
"""
|
||||
Data collator that will dynamically pad the inputs received.
|
||||
Args:
|
||||
processor (:class:`~transformers.AutoProcessor`)
|
||||
The processor used for proccessing the data.
|
||||
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
|
||||
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
|
||||
among:
|
||||
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
||||
sequence if provided).
|
||||
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
|
||||
maximum acceptable input length for the model if that argument is not provided.
|
||||
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
|
||||
different lengths).
|
||||
max_length (:obj:`int`, `optional`):
|
||||
Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
|
||||
max_length_labels (:obj:`int`, `optional`):
|
||||
Maximum length of the ``labels`` returned list and optionally padding length (see above).
|
||||
pad_to_multiple_of (:obj:`int`, `optional`):
|
||||
If set will pad the sequence to a multiple of the provided value.
|
||||
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
|
||||
7.5 (Volta).
|
||||
"""
|
||||
|
||||
processor: AutoProcessor
|
||||
padding: Union[bool, str] = "longest"
|
||||
pad_to_multiple_of: Optional[int] = None
|
||||
pad_to_multiple_of_labels: Optional[int] = None
|
||||
|
||||
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
||||
# split inputs and labels since they have to be of different lenghts and need
|
||||
# different padding methods
|
||||
input_features = [{"input_values": feature["input_values"]} for feature in features]
|
||||
label_features = [{"input_ids": feature["labels"]} for feature in features]
|
||||
|
||||
batch = self.processor.pad(
|
||||
input_features,
|
||||
padding=self.padding,
|
||||
pad_to_multiple_of=self.pad_to_multiple_of,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
with self.processor.as_target_processor():
|
||||
labels_batch = self.processor.pad(
|
||||
label_features,
|
||||
padding=self.padding,
|
||||
pad_to_multiple_of=self.pad_to_multiple_of_labels,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
# replace padding with -100 to ignore loss correctly
|
||||
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
|
||||
|
||||
batch["labels"] = labels
|
||||
|
||||
return batch
|
||||
|
||||
|
||||
def create_vocabulary_from_data(
|
||||
datasets: DatasetDict,
|
||||
word_delimiter_token: Optional[str] = None,
|
||||
unk_token: Optional[str] = None,
|
||||
pad_token: Optional[str] = None,
|
||||
):
|
||||
# Given training and test labels create vocabulary
|
||||
def extract_all_chars(batch):
|
||||
all_text = " ".join(batch["target_text"])
|
||||
vocab = list(set(all_text))
|
||||
return {"vocab": [vocab], "all_text": [all_text]}
|
||||
|
||||
vocabs = datasets.map(
|
||||
extract_all_chars,
|
||||
batched=True,
|
||||
batch_size=-1,
|
||||
keep_in_memory=True,
|
||||
remove_columns=datasets["train"].column_names,
|
||||
)
|
||||
|
||||
# take union of all unique characters in each dataset
|
||||
vocab_set = functools.reduce(
|
||||
lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
|
||||
)
|
||||
|
||||
vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
|
||||
|
||||
# replace white space with delimiter token
|
||||
if word_delimiter_token is not None:
|
||||
vocab_dict[word_delimiter_token] = vocab_dict[" "]
|
||||
del vocab_dict[" "]
|
||||
|
||||
# add unk and pad token
|
||||
if unk_token is not None:
|
||||
vocab_dict[unk_token] = len(vocab_dict)
|
||||
|
||||
if pad_token is not None:
|
||||
vocab_dict[pad_token] = len(vocab_dict)
|
||||
|
||||
return vocab_dict
|
||||
|
||||
|
||||
def main():
|
||||
# See all possible arguments in src/transformers/training_args.py
|
||||
# or by passing the --help flag to this script.
|
||||
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||||
|
||||
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
||||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
||||
# If we pass only one argument to the script and it's the path to a json file,
|
||||
# let's parse it to get our arguments.
|
||||
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||||
else:
|
||||
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||||
|
||||
# Detecting last checkpoint.
|
||||
last_checkpoint = None
|
||||
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
||||
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
||||
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
||||
raise ValueError(
|
||||
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
||||
"Use --overwrite_output_dir to overcome."
|
||||
)
|
||||
elif last_checkpoint is not None:
|
||||
logger.info(
|
||||
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
||||
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
||||
)
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
handlers=[logging.StreamHandler(sys.stdout)],
|
||||
)
|
||||
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
|
||||
|
||||
# Log on each process the small summary:
|
||||
logger.warning(
|
||||
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
||||
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
||||
)
|
||||
# Set the verbosity to info of the Transformers logger (on main process only):
|
||||
if is_main_process(training_args.local_rank):
|
||||
transformers.utils.logging.set_verbosity_info()
|
||||
logger.info("Training/evaluation parameters %s", training_args)
|
||||
|
||||
# Set seed before initializing model.
|
||||
set_seed(training_args.seed)
|
||||
|
||||
# Pre-processing dataset
|
||||
import re
|
||||
def filter_numeric(entry):
|
||||
return (
|
||||
"0" not in entry["text"]
|
||||
and "1" not in entry["text"]
|
||||
and "2" not in entry["text"]
|
||||
and "3" not in entry["text"]
|
||||
and "4" not in entry["text"]
|
||||
and "5" not in entry["text"]
|
||||
and "6" not in entry["text"]
|
||||
and "7" not in entry["text"]
|
||||
and "8" not in entry["text"]
|
||||
and "9" not in entry["text"]
|
||||
)
|
||||
|
||||
def filter_inaudible(entry):
|
||||
return not re.search("\d|<inaudible>", entry["text"], flags=re.IGNORECASE)
|
||||
|
||||
def filter_nynorsk(entry):
|
||||
return re.search("nb-no", entry["sentence_language_code"], flags=re.IGNORECASE)
|
||||
|
||||
def filter_tooshort(entry):
|
||||
#print(f"The audio sample ({entry["audio"]["path"]}) is too small, and has been omitted. "
|
||||
return (len(entry["text"]) <= len(entry["audio"]["array"]) // 320) and (len(entry["text"].strip()) >= 3)
|
||||
|
||||
def map_dataset(entry):
|
||||
batch = {"text": entry["text"].lower()}
|
||||
batch["text"] = re.sub('[áàâ]', 'a', batch["text"])
|
||||
batch["text"] = re.sub('[ä]', 'æ', batch["text"])
|
||||
batch["text"] = re.sub('[éèëê]', 'e', batch["text"])
|
||||
batch["text"] = re.sub('[íìïî]', 'i', batch["text"])
|
||||
batch["text"] = re.sub('[óòöô]', 'o', batch["text"])
|
||||
batch["text"] = re.sub('[ö]', 'ø', batch["text"])
|
||||
batch["text"] = re.sub('[ç]', 'c', batch["text"])
|
||||
batch["text"] = re.sub('[úùüû]', 'u', batch["text"])
|
||||
batch["text"] = re.sub('\s', ' ', batch["text"])
|
||||
batch["text"] = re.sub('<ee>', 'eee', batch["text"])
|
||||
batch["text"] = re.sub('<qq>', 'qqq', batch["text"])
|
||||
batch["text"] = re.sub('<mm>', 'mmm', batch["text"])
|
||||
# batch["text"] = re.sub('<inaudible>', '?', batch["text"])
|
||||
if "<" in batch["text"]:
|
||||
raise ValueError(batch["text"])
|
||||
return batch
|
||||
|
||||
def filter_extra(entry):
|
||||
return (entry["text"].lower().replace("*", "").replace("#", "") == entry["transsentence_text"].lower().replace("*", "").replace("#", ""))
|
||||
|
||||
|
||||
# 1. First, let's load the dataset
|
||||
raw_datasets = DatasetDict()
|
||||
|
||||
if training_args.do_train:
|
||||
raw_datasets["train"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=data_args.train_split_name,
|
||||
use_auth_token=data_args.use_auth_token,
|
||||
).shuffle()
|
||||
raw_datasets["train"] = raw_datasets["train"].filter(filter_inaudible).filter(filter_numeric).filter(filter_tooshort)
|
||||
raw_datasets["train"] = raw_datasets["train"].map(map_dataset)
|
||||
|
||||
# Expand dataset
|
||||
if "nynorsk" in data_args.dataset_config_name:
|
||||
extra_dataset = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name.rsplit("_", 1)[0] + "_bokmaal",
|
||||
split=data_args.train_split_name,
|
||||
use_auth_token=data_args.use_auth_token,
|
||||
).shuffle()
|
||||
extra_dataset = extra_dataset.filter(filter_inaudible).filter(filter_numeric).filter(filter_tooshort).filter(filter_extra)
|
||||
extra_dataset = extra_dataset.map(map_dataset)
|
||||
raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], extra_dataset]).shuffle()
|
||||
logger.info(f"Added extra {extra_dataset.num_rows} samples")
|
||||
|
||||
|
||||
if data_args.audio_column_name not in raw_datasets["train"].column_names:
|
||||
raise ValueError(
|
||||
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
|
||||
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
||||
f"{', '.join(raw_datasets['train'].column_names)}."
|
||||
)
|
||||
|
||||
if data_args.text_column_name not in raw_datasets["train"].column_names:
|
||||
raise ValueError(
|
||||
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
|
||||
"Make sure to set `--text_column_name` to the correct text column - one of "
|
||||
f"{', '.join(raw_datasets['train'].column_names)}."
|
||||
)
|
||||
|
||||
if data_args.max_train_samples is not None:
|
||||
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
|
||||
|
||||
if training_args.do_eval:
|
||||
raw_datasets["eval"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=data_args.eval_split_name,
|
||||
use_auth_token=data_args.use_auth_token,
|
||||
).shuffle()
|
||||
raw_datasets["eval"] = raw_datasets["eval"].filter(filter_inaudible).filter(filter_numeric).filter(filter_tooshort)
|
||||
raw_datasets["eval"] = raw_datasets["eval"].map(map_dataset)
|
||||
|
||||
if data_args.max_eval_samples is not None:
|
||||
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
||||
|
||||
|
||||
# 2. We remove some special characters from the datasets
|
||||
# that make training complicated and do not help in transcribing the speech
|
||||
# E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
|
||||
# that could be easily picked up by the model
|
||||
#chars_to_ignore_regex = (
|
||||
# f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
|
||||
#)
|
||||
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\<EFBFBD>\'\–\_\\\+\#\/]'
|
||||
|
||||
text_column_name = data_args.text_column_name
|
||||
|
||||
def remove_special_characters(batch):
|
||||
if chars_to_ignore_regex is not None:
|
||||
batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
|
||||
else:
|
||||
batch["target_text"] = batch[text_column_name].lower() + " "
|
||||
return batch
|
||||
|
||||
with training_args.main_process_first(desc="dataset map special characters removal"):
|
||||
raw_datasets = raw_datasets.map(
|
||||
remove_special_characters,
|
||||
remove_columns=[text_column_name],
|
||||
desc="remove special characters from datasets",
|
||||
)
|
||||
|
||||
# save special tokens for tokenizer
|
||||
word_delimiter_token = data_args.word_delimiter_token
|
||||
unk_token = data_args.unk_token
|
||||
pad_token = data_args.pad_token
|
||||
|
||||
# 3. Next, let's load the config as we might need it to create
|
||||
# the tokenizer
|
||||
# load config
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
|
||||
)
|
||||
|
||||
# 4. Next, if no tokenizer file is defined,
|
||||
# we create the vocabulary of the model by extracting all unique characters from
|
||||
# the training and evaluation datasets
|
||||
# We need to make sure that only first rank saves vocabulary
|
||||
# make sure all processes wait until vocab is created
|
||||
tokenizer_name_or_path = model_args.tokenizer_name_or_path
|
||||
tokenizer_kwargs = {}
|
||||
if tokenizer_name_or_path is None:
|
||||
# save vocab in training output dir
|
||||
tokenizer_name_or_path = training_args.output_dir
|
||||
|
||||
vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
|
||||
|
||||
with training_args.main_process_first():
|
||||
if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
|
||||
os.remove(vocab_file)
|
||||
|
||||
with training_args.main_process_first(desc="dataset map vocabulary creation"):
|
||||
if not os.path.isfile(vocab_file):
|
||||
os.makedirs(tokenizer_name_or_path, exist_ok=True)
|
||||
vocab_dict = create_vocabulary_from_data(
|
||||
raw_datasets,
|
||||
word_delimiter_token=word_delimiter_token,
|
||||
unk_token=unk_token,
|
||||
pad_token=pad_token,
|
||||
)
|
||||
|
||||
# save vocab dict to be loaded into tokenizer
|
||||
with open(vocab_file, "w") as file:
|
||||
json.dump(vocab_dict, file)
|
||||
|
||||
# if tokenizer has just been created
|
||||
# it is defined by `tokenizer_class` if present in config else by `model_type`
|
||||
tokenizer_kwargs = {
|
||||
"config": config if config.tokenizer_class is not None else None,
|
||||
"tokenizer_type": config.model_type if config.tokenizer_class is None else None,
|
||||
"unk_token": unk_token,
|
||||
"pad_token": pad_token,
|
||||
"word_delimiter_token": word_delimiter_token,
|
||||
}
|
||||
|
||||
# 5. Now we can instantiate the feature extractor, tokenizer and model
|
||||
# Note for distributed training, the .from_pretrained methods guarantee that only
|
||||
# one local process can concurrently download model & vocab.
|
||||
|
||||
# load feature_extractor and tokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
tokenizer_name_or_path,
|
||||
use_auth_token=data_args.use_auth_token,
|
||||
**tokenizer_kwargs,
|
||||
)
|
||||
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
||||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
|
||||
)
|
||||
|
||||
# adapt config
|
||||
config.update(
|
||||
{
|
||||
"feat_proj_dropout": model_args.feat_proj_dropout,
|
||||
"attention_dropout": model_args.attention_dropout,
|
||||
"hidden_dropout": model_args.hidden_dropout,
|
||||
"final_dropout": model_args.final_dropout,
|
||||
"mask_time_prob": model_args.mask_time_prob,
|
||||
"mask_time_length": model_args.mask_time_length,
|
||||
"mask_feature_prob": model_args.mask_feature_prob,
|
||||
"mask_feature_length": model_args.mask_feature_length,
|
||||
"gradient_checkpointing": training_args.gradient_checkpointing,
|
||||
"layerdrop": model_args.layerdrop,
|
||||
"ctc_loss_reduction": model_args.ctc_loss_reduction,
|
||||
"ctc_zero_infinity": model_args.ctc_zero_infinity,
|
||||
"pad_token_id": tokenizer.pad_token_id,
|
||||
"vocab_size": len(tokenizer),
|
||||
"activation_dropout": model_args.activation_dropout,
|
||||
}
|
||||
)
|
||||
|
||||
# create model
|
||||
model = AutoModelForCTC.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
config=config,
|
||||
use_auth_token=data_args.use_auth_token,
|
||||
)
|
||||
|
||||
# freeze encoder
|
||||
if model_args.freeze_feature_encoder:
|
||||
model.freeze_feature_encoder()
|
||||
|
||||
# 6. Now we preprocess the datasets including loading the audio, resampling and normalization
|
||||
# Thankfully, `datasets` takes care of automatically loading and resampling the audio,
|
||||
# so that we just need to set the correct target sampling rate and normalize the input
|
||||
# via the `feature_extractor`
|
||||
|
||||
# make sure that dataset decodes audio with correct sampling rate
|
||||
dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
|
||||
if dataset_sampling_rate != feature_extractor.sampling_rate:
|
||||
raw_datasets = raw_datasets.cast_column(
|
||||
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
|
||||
)
|
||||
|
||||
# derive max & min input length for sample rate & max duration
|
||||
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
||||
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
||||
audio_column_name = data_args.audio_column_name
|
||||
num_workers = data_args.preprocessing_num_workers
|
||||
|
||||
# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
|
||||
phoneme_language = data_args.phoneme_language
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# We need to read the audio files as arrays and tokenize the targets.
|
||||
def prepare_dataset(batch):
|
||||
# load audio
|
||||
sample = batch[audio_column_name]
|
||||
|
||||
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
|
||||
batch["input_values"] = inputs.input_values[0]
|
||||
batch["input_length"] = len(batch["input_values"])
|
||||
|
||||
# encode targets
|
||||
additional_kwargs = {}
|
||||
if phoneme_language is not None:
|
||||
additional_kwargs["phonemizer_lang"] = phoneme_language
|
||||
|
||||
batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
|
||||
return batch
|
||||
|
||||
with training_args.main_process_first(desc="dataset map preprocessing"):
|
||||
vectorized_datasets = raw_datasets.map(
|
||||
prepare_dataset,
|
||||
remove_columns=next(iter(raw_datasets.values())).column_names,
|
||||
num_proc=num_workers,
|
||||
desc="preprocess datasets",
|
||||
)
|
||||
|
||||
def is_audio_in_length_range(length):
|
||||
return length > min_input_length and length < max_input_length
|
||||
|
||||
# filter data that is shorter than min_input_length
|
||||
vectorized_datasets = vectorized_datasets.filter(
|
||||
is_audio_in_length_range,
|
||||
num_proc=num_workers,
|
||||
input_columns=["input_length"],
|
||||
)
|
||||
|
||||
# 7. Next, we can prepare the training.
|
||||
# Let's use word error rate (WER) as our evaluation metric,
|
||||
# instantiate a data collator and the trainer
|
||||
|
||||
# Define evaluation metrics during training, *i.e.* word error rate, character error rate
|
||||
eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
|
||||
|
||||
# for large datasets it is advised to run the preprocessing on a
|
||||
# single machine first with ``args.preprocessing_only`` since there will mostly likely
|
||||
# be a timeout when running the script in distributed mode.
|
||||
# In a second step ``args.preprocessing_only`` can then be set to `False` to load the
|
||||
# cached dataset
|
||||
if data_args.preprocessing_only:
|
||||
logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
|
||||
return
|
||||
|
||||
def compute_metrics(pred):
|
||||
pred_logits = pred.predictions
|
||||
pred_ids = np.argmax(pred_logits, axis=-1)
|
||||
|
||||
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
|
||||
|
||||
pred_str = tokenizer.batch_decode(pred_ids)
|
||||
# we do not want to group tokens when computing the metrics
|
||||
label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
|
||||
|
||||
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
|
||||
|
||||
return metrics
|
||||
|
||||
# Now save everything to be able to create a single processor later
|
||||
if is_main_process(training_args.local_rank):
|
||||
# save feature extractor, tokenizer and config
|
||||
feature_extractor.save_pretrained(training_args.output_dir)
|
||||
tokenizer.save_pretrained(training_args.output_dir)
|
||||
config.save_pretrained(training_args.output_dir)
|
||||
|
||||
try:
|
||||
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
||||
except (OSError, KeyError):
|
||||
warnings.warn(
|
||||
"Loading a processor from a feature extractor config that does not"
|
||||
" include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
|
||||
" attribute to your `preprocessor_config.json` file to suppress this warning: "
|
||||
" `'processor_class': 'Wav2Vec2Processor'`",
|
||||
FutureWarning,
|
||||
)
|
||||
processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
|
||||
|
||||
# Instantiate custom data collator
|
||||
data_collator = DataCollatorCTCWithPadding(processor=processor)
|
||||
|
||||
# Initialize Trainer
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
data_collator=data_collator,
|
||||
args=training_args,
|
||||
compute_metrics=compute_metrics,
|
||||
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
|
||||
eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
|
||||
tokenizer=feature_extractor,
|
||||
)
|
||||
|
||||
# 8. Finally, we can start training
|
||||
|
||||
# Training
|
||||
if training_args.do_train:
|
||||
|
||||
# use last checkpoint if exist
|
||||
if last_checkpoint is not None:
|
||||
checkpoint = last_checkpoint
|
||||
elif os.path.isdir(model_args.model_name_or_path):
|
||||
checkpoint = model_args.model_name_or_path
|
||||
else:
|
||||
checkpoint = None
|
||||
|
||||
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
||||
trainer.save_model()
|
||||
|
||||
metrics = train_result.metrics
|
||||
max_train_samples = (
|
||||
data_args.max_train_samples
|
||||
if data_args.max_train_samples is not None
|
||||
else len(vectorized_datasets["train"])
|
||||
)
|
||||
metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
|
||||
|
||||
trainer.log_metrics("train", metrics)
|
||||
trainer.save_metrics("train", metrics)
|
||||
trainer.save_state()
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
if training_args.do_eval:
|
||||
logger.info("*** Evaluate ***")
|
||||
metrics = trainer.evaluate()
|
||||
max_eval_samples = (
|
||||
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
|
||||
)
|
||||
metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
|
||||
|
||||
trainer.log_metrics("eval", metrics)
|
||||
trainer.save_metrics("eval", metrics)
|
||||
|
||||
# Write model card and (optionally) push to hub
|
||||
config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
|
||||
kwargs = {
|
||||
"finetuned_from": model_args.model_name_or_path,
|
||||
"tasks": "speech-recognition",
|
||||
"tags": ["automatic-speech-recognition", data_args.dataset_name],
|
||||
"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
|
||||
"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
|
||||
}
|
||||
if "common_voice" in data_args.dataset_name:
|
||||
kwargs["language"] = config_name
|
||||
|
||||
if training_args.push_to_hub:
|
||||
trainer.push_to_hub(**kwargs)
|
||||
else:
|
||||
trainer.create_model_card(**kwargs)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:17d568c75773772f7a011c3ec735444c71804b3ac54977708dbcd4cb07d18bae
|
||||
size 4750
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:04228cc292432da366ad813973e42cf76c8ee8078b9b5618af00cc5335c59a5f
|
||||
size 71326
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4b12aa9345831f7dd28e4397c098df2a06dd14e42433f9d333f920f5128faa97
|
||||
size 364
|
||||
1
special_tokens_map.json
Normal file
1
special_tokens_map.json
Normal file
@@ -0,0 +1 @@
|
||||
{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}
|
||||
1
tokenizer_config.json
Normal file
1
tokenizer_config.json
Normal file
@@ -0,0 +1 @@
|
||||
{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2ProcessorWithLM"}
|
||||
8
train_results.json
Normal file
8
train_results.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"epoch": 60.0,
|
||||
"train_loss": 0.29242472694807237,
|
||||
"train_runtime": 84446.646,
|
||||
"train_samples": 11961,
|
||||
"train_samples_per_second": 8.498,
|
||||
"train_steps_per_second": 0.355
|
||||
}
|
||||
2350
trainer_state.json
Normal file
2350
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1790380ec4edb71deaad48f7b85f1c40a1cb6a6a8eb4a57612cb779847669157
|
||||
size 3055
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
@@ -0,0 +1 @@
|
||||
{"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6, "g": 7, "h": 8, "i": 9, "j": 10, "k": 11, "l": 12, "m": 13, "n": 14, "o": 15, "p": 16, "q": 17, "r": 18, "s": 19, "t": 20, "u": 21, "v": 22, "w": 23, "x": 24, "y": 25, "z": 26, "å": 27, "æ": 28, "ø": 29, "|": 0, "[UNK]": 30, "[PAD]": 31}
|
||||
Reference in New Issue
Block a user