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Model: Plim/xls-r-300m-fr Source: Original Platform
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checkpoint-*/
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.ipynb_checkpoints/
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wandb
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121
README.md
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README.md
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---
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language:
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- fr
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license: apache-2.0
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tags:
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- automatic-speech-recognition
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- mozilla-foundation/common_voice_7_0
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- generated_from_trainer
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- robust-speech-event
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- hf-asr-leaderboard
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datasets:
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- mozilla-foundation/common_voice_7_0
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model-index:
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- name: XLS-R-300M - French
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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: Common Voice 7
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type: mozilla-foundation/common_voice_7_0
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args: fr
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metrics:
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- name: Test WER
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type: wer
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value: 24.56
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- name: Test CER
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type: cer
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value: 7.3
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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: Robust Speech Event - Dev Data
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type: speech-recognition-community-v2/dev_data
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args: fr
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metrics:
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- name: Test WER
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type: wer
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value: 63.62
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- name: Test CER
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type: cer
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value: 17.2
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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: Robust Speech Event - Test Data
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type: speech-recognition-community-v2/eval_data
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args: fr
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metrics:
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- name: Test WER
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type: wer
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value: 66.45
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---
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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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## Model description
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FR dataset.
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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: 7.5e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 128
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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: 2.0
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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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| 3.495 | 0.16 | 500 | 3.3883 | 1.0 |
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| 2.9095 | 0.32 | 1000 | 2.9152 | 1.0000 |
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| 1.8434 | 0.49 | 1500 | 1.0473 | 0.7446 |
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| 1.4298 | 0.65 | 2000 | 0.5729 | 0.5130 |
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| 1.1937 | 0.81 | 2500 | 0.3795 | 0.3450 |
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| 1.1248 | 0.97 | 3000 | 0.3321 | 0.3052 |
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| 1.0835 | 1.13 | 3500 | 0.3038 | 0.2805 |
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| 1.0479 | 1.3 | 4000 | 0.2910 | 0.2689 |
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| 1.0413 | 1.46 | 4500 | 0.2798 | 0.2593 |
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| 1.014 | 1.62 | 5000 | 0.2727 | 0.2512 |
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| 1.004 | 1.78 | 5500 | 0.2646 | 0.2471 |
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| 0.9949 | 1.94 | 6000 | 0.2619 | 0.2457 |
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It achieves the best result on STEP 6000 on the validation set:
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- Loss: 0.2619
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- Wer: 0.2457
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### Framework versions
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- Transformers 4.17.0.dev0
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- Pytorch 1.10.2+cu102
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- Datasets 1.18.2.dev0
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- Tokenizers 0.11.0
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### Evaluation Commands
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1. To evaluate on `mozilla-foundation/common_voice_7` with split `test`
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```bash
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python eval.py --model_id Plim/xls-r-300m-fr --dataset mozilla-foundation/common_voice_7_0 --config fr --split test
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```
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2. To evaluate on `speech-recognition-community-v2/dev_data`
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```bash
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python eval.py --model_id Plim/xls-r-300m-fr --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
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```
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14
all_results.json
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all_results.json
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{
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"epoch": 2.0,
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"eval_loss": 0.26187804341316223,
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"eval_runtime": 722.142,
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"eval_samples": 15941,
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"eval_samples_per_second": 22.075,
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"eval_steps_per_second": 1.381,
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"eval_wer": 0.24574541380398318,
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"train_loss": 1.788894365302016,
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"train_runtime": 52105.5599,
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"train_samples": 395042,
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"train_samples_per_second": 15.163,
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"train_steps_per_second": 0.118
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}
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107
config.json
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config.json
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{
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"_name_or_path": "facebook/wav2vec2-xls-r-300m",
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"activation_dropout": 0.1,
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"add_adapter": false,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForCTC"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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"codevector_dim": 768,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": true,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": false,
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"diversity_loss_weight": 0.1,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.0,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"hidden_act": "gelu",
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"hidden_dropout": 0.0,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.0,
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"mask_feature_length": 64,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.25,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.75,
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"model_type": "wav2vec2",
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"num_adapter_layers": 3,
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"num_attention_heads": 16,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"output_hidden_size": 1024,
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"pad_token_id": 45,
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"proj_codevector_dim": 768,
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"tdnn_dilation": [
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1,
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2,
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3,
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1,
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1
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],
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"tdnn_dim": [
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512,
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512,
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512,
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512,
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1500
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],
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"tdnn_kernel": [
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5,
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3,
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3,
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1,
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1
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],
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"torch_dtype": "float32",
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"transformers_version": "4.17.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size": 46,
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"xvector_output_dim": 512
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}
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eval.py
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#!/usr/bin/env python3
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import argparse
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import re
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from typing import Dict
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from datasets import Audio, Dataset, load_dataset, load_metric
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from transformers import AutoFeatureExtractor, pipeline
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def log_results(result: Dataset, args: Dict[str, str]):
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"""DO NOT CHANGE. This function computes and logs the result metrics."""
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log_outputs = args.log_outputs
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dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
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# load metric
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wer = load_metric("wer")
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cer = load_metric("cer")
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# compute metrics
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wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
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cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
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# print & log results
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result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
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print(result_str)
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with open(f"{dataset_id}_eval_results.txt", "w") as f:
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f.write(result_str)
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# log all results in text file. Possibly interesting for analysis
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if log_outputs is not None:
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pred_file = f"log_{dataset_id}_predictions.txt"
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target_file = f"log_{dataset_id}_targets.txt"
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with open(pred_file, "w") as p, open(target_file, "w") as t:
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# mapping function to write output
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def write_to_file(batch, i):
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p.write(f"{i}" + "\n")
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p.write(batch["prediction"] + "\n")
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t.write(f"{i}" + "\n")
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t.write(batch["target"] + "\n")
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result.map(write_to_file, with_indices=True)
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def normalize_text(text: str) -> str:
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"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
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chars_to_ignore_regex = '[^a-zàâäçéèêëîïôöùûüÿ\'’ ]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
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text = re.sub(chars_to_ignore_regex, "", text.lower()).replace('’', "'")
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# In addition, we can normalize the target text, e.g. removing new lines characters etc...
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# note that order is important here!
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token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
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for t in token_sequences_to_ignore:
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text = " ".join(text.split(t))
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return text
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def main(args):
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# load dataset
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dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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# for testing: only process the first two examples as a test
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# dataset = dataset.select(range(10))
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# load processor
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feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
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sampling_rate = feature_extractor.sampling_rate
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# resample audio
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dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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# load eval pipeline
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asr = pipeline("automatic-speech-recognition", model=args.model_id)
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# map function to decode audio
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def map_to_pred(batch):
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prediction = asr(
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batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
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)
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batch["prediction"] = prediction["text"]
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batch["target"] = normalize_text(batch["sentence"])
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return batch
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# run inference on all examples
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result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
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# compute and log_results
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# do not change function below
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log_results(result, args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
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)
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parser.add_argument(
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"--dataset",
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type=str,
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required=True,
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help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
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)
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parser.add_argument(
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"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
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)
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parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
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parser.add_argument(
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"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
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)
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parser.add_argument(
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"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
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)
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parser.add_argument(
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"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
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)
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args = parser.parse_args()
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main(args)
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eval_results.json
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eval_results.json
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{
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"epoch": 2.0,
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"eval_loss": 0.26187804341316223,
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"eval_runtime": 722.142,
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"eval_samples": 15941,
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"eval_samples_per_second": 22.075,
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"eval_steps_per_second": 1.381,
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"eval_wer": 0.24574541380398318
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}
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31882
log_mozilla-foundation_common_voice_7_0_fr_test_predictions.txt
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log_mozilla-foundation_common_voice_7_0_fr_test_predictions.txt
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log_mozilla-foundation_common_voice_7_0_fr_test_targets.txt
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log_mozilla-foundation_common_voice_7_0_fr_test_targets.txt
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WER: 0.24561764914155493
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CER: 0.07285207821118034
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preprocessor_config.json
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preprocessor_config.json
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{
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"do_normalize": true,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0,
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"return_attention_mask": true,
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"sampling_rate": 16000
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}
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3
pytorch_model.bin
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3
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:fae4b50fea64734470da322cdd4b51d1e72024c521098f4fce28c513870e1017
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size 1262112241
|
||||
37
run.sh
Normal file
37
run.sh
Normal file
@@ -0,0 +1,37 @@
|
||||
export WANDB_PROJECT="xls-r-300-fr"
|
||||
python run_speech_recognition_ctc.py \
|
||||
--activation_dropout="0.1" \
|
||||
--dataset_name="mozilla-foundation/common_voice_7_0" \
|
||||
--dataset_config_name="fr" \
|
||||
--eval_steps="500" \
|
||||
--evaluation_strategy="steps" \
|
||||
--feat_proj_dropout="0.0" \
|
||||
--freeze_feature_encoder \
|
||||
--fp16 \
|
||||
--gradient_accumulation_steps="8" \
|
||||
--gradient_checkpointing \
|
||||
--group_by_length \
|
||||
--layerdrop="0.0" \
|
||||
--learning_rate="7.5e-5" \
|
||||
--length_column_name="input_length" \
|
||||
--load_best_model_at_end \
|
||||
--logging_steps="100" \
|
||||
--mask_feature_length="64" \
|
||||
--mask_feature_prob="0.25" \
|
||||
--mask_time_length="10" \
|
||||
--mask_time_prob="0.75" \
|
||||
--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
|
||||
--num_train_epochs="2.0" \
|
||||
--output_dir="./" \
|
||||
--overwrite_output_dir \
|
||||
--per_device_train_batch_size="16" \
|
||||
--per_device_eval_batch_size="16" \
|
||||
--preprocessing_num_workers="4" \
|
||||
--push_to_hub \
|
||||
--report_to="wandb" \
|
||||
--save_steps="500" \
|
||||
--save_total_limit="3" \
|
||||
--text_column_name="sentence" \
|
||||
--use_auth_token \
|
||||
--warmup_steps="2000" \
|
||||
--do_train --do_eval
|
||||
734
run_speech_recognition_ctc.py
Normal file
734
run_speech_recognition_ctc.py
Normal file
@@ -0,0 +1,734 @@
|
||||
#!/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
|
||||
|
||||
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'."}
|
||||
)
|
||||
|
||||
|
||||
@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 'test'"
|
||||
},
|
||||
)
|
||||
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)
|
||||
|
||||
# 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,
|
||||
)
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
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 = '[^a-zàâäçéèêëîïôöùûüÿ\'’ ]'
|
||||
text_column_name = data_args.text_column_name
|
||||
|
||||
def remove_and_replace_special_characters(batch):
|
||||
batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name].lower()).replace('’', "'") + " "
|
||||
return batch
|
||||
|
||||
with training_args.main_process_first(desc="dataset map special characters removal"):
|
||||
raw_datasets = raw_datasets.map(
|
||||
remove_and_replace_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,
|
||||
"eos_token": None,
|
||||
"bos_token": None,
|
||||
"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,
|
||||
"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()
|
||||
1
special_tokens_map.json
Normal file
1
special_tokens_map.json
Normal file
@@ -0,0 +1 @@
|
||||
{"unk_token": "[UNK]", "pad_token": "[PAD]"}
|
||||
@@ -0,0 +1,2 @@
|
||||
WER: 0.6362465106291604
|
||||
CER: 0.17202817283379465
|
||||
1
tokenizer_config.json
Normal file
1
tokenizer_config.json
Normal file
@@ -0,0 +1 @@
|
||||
{"unk_token": "[UNK]", "bos_token": null, "eos_token": null, "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer"}
|
||||
8
train_results.json
Normal file
8
train_results.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"epoch": 2.0,
|
||||
"train_loss": 1.788894365302016,
|
||||
"train_runtime": 52105.5599,
|
||||
"train_samples": 395042,
|
||||
"train_samples_per_second": 15.163,
|
||||
"train_steps_per_second": 0.118
|
||||
}
|
||||
499
trainer_state.json
Normal file
499
trainer_state.json
Normal file
@@ -0,0 +1,499 @@
|
||||
{
|
||||
"best_metric": 0.26187804341316223,
|
||||
"best_model_checkpoint": "./checkpoint-6000",
|
||||
"epoch": 1.9998784982382245,
|
||||
"global_step": 6172,
|
||||
"is_hyper_param_search": false,
|
||||
"is_local_process_zero": true,
|
||||
"is_world_process_zero": true,
|
||||
"log_history": [
|
||||
{
|
||||
"epoch": 0.03,
|
||||
"learning_rate": 3.7499999999999997e-06,
|
||||
"loss": 12.1043,
|
||||
"step": 100
|
||||
},
|
||||
{
|
||||
"epoch": 0.06,
|
||||
"learning_rate": 7.499999999999999e-06,
|
||||
"loss": 6.4771,
|
||||
"step": 200
|
||||
},
|
||||
{
|
||||
"epoch": 0.1,
|
||||
"learning_rate": 1.1249999999999999e-05,
|
||||
"loss": 4.4866,
|
||||
"step": 300
|
||||
},
|
||||
{
|
||||
"epoch": 0.13,
|
||||
"learning_rate": 1.4999999999999999e-05,
|
||||
"loss": 3.8842,
|
||||
"step": 400
|
||||
},
|
||||
{
|
||||
"epoch": 0.16,
|
||||
"learning_rate": 1.8712499999999997e-05,
|
||||
"loss": 3.495,
|
||||
"step": 500
|
||||
},
|
||||
{
|
||||
"epoch": 0.16,
|
||||
"eval_loss": 3.3882696628570557,
|
||||
"eval_runtime": 721.337,
|
||||
"eval_samples_per_second": 22.099,
|
||||
"eval_steps_per_second": 1.382,
|
||||
"eval_wer": 1.0,
|
||||
"step": 500
|
||||
},
|
||||
{
|
||||
"epoch": 0.19,
|
||||
"learning_rate": 2.2462499999999997e-05,
|
||||
"loss": 3.171,
|
||||
"step": 600
|
||||
},
|
||||
{
|
||||
"epoch": 0.23,
|
||||
"learning_rate": 2.6212499999999997e-05,
|
||||
"loss": 3.0275,
|
||||
"step": 700
|
||||
},
|
||||
{
|
||||
"epoch": 0.26,
|
||||
"learning_rate": 2.99625e-05,
|
||||
"loss": 2.9681,
|
||||
"step": 800
|
||||
},
|
||||
{
|
||||
"epoch": 0.29,
|
||||
"learning_rate": 3.37125e-05,
|
||||
"loss": 2.9347,
|
||||
"step": 900
|
||||
},
|
||||
{
|
||||
"epoch": 0.32,
|
||||
"learning_rate": 3.7462499999999996e-05,
|
||||
"loss": 2.9095,
|
||||
"step": 1000
|
||||
},
|
||||
{
|
||||
"epoch": 0.32,
|
||||
"eval_loss": 2.9152133464813232,
|
||||
"eval_runtime": 718.1623,
|
||||
"eval_samples_per_second": 22.197,
|
||||
"eval_steps_per_second": 1.388,
|
||||
"eval_wer": 0.9999871219487068,
|
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3
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1
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||||
Reference in New Issue
Block a user