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Model: comodoro/wav2vec2-xls-r-300m-sk-cv8 Source: Original Platform
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131
README.md
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README.md
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---
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language:
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- sk
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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_8_0
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- robust-speech-event
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- xlsr-fine-tuning-week
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- hf-asr-leaderboard
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datasets:
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- common_voice
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model-index:
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- name: Slovak comodoro Wav2Vec2 XLSR 300M CV8
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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 8
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type: mozilla-foundation/common_voice_8_0
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args: sk
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metrics:
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- name: Test WER
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type: wer
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value: 49.6
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- name: Test CER
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type: cer
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value: 13.3
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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: sk
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metrics:
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- name: Test WER
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type: wer
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value: 81.7
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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: sk
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metrics:
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- name: Test WER
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type: wer
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value: 80.26
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---
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# wav2vec2-xls-r-300m-cs-cv8
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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 common_voice 8.0 dataset.
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It achieves the following results on the evaluation set:
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- WER: 0.49575384615384616
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- CER: 0.13333333333333333
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## Usage
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The model can be used directly (without a language model) as follows:
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```python
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import torch
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import torchaudio
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "sk", split="test[:2%]")
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processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
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model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def speech_file_to_array_fn(batch):
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speech_array, sampling_rate = torchaudio.load(batch["path"])
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batch["speech"] = resampler(speech_array).squeeze().numpy()
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return batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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print("Prediction:", processor.batch_decode(predicted_ids))
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print("Reference:", test_dataset[:2]["sentence"])
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```
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## Evaluation
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The model can be evaluated using the attached `eval.py` script:
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```
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python eval.py --model_id comodoro/wav2vec2-xls-r-300m-sk-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config sk
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```
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## Training and evaluation data
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The Common Voice 8.0 `train` and `validation` datasets were used for training
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 7e-4
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- train_batch_size: 32
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 20
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- total_train_batch_size: 640
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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: 500
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- num_epochs: 50
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.16.0.dev0
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- Pytorch 1.10.1+cu102
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- Datasets 1.17.1.dev0
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- Tokenizers 0.11.0
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1
added_tokens.json
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added_tokens.json
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{"<s>": 47, "</s>": 48}
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108
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": 0.0,
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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": 0.1,
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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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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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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": 0.1,
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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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": 46,
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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": "4.16.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size": 49,
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"xvector_output_dim": 512
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}
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eval.py
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eval.py
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#!/usr/bin/env python3
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from datasets import load_dataset, load_metric, Audio, Dataset
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from transformers import pipeline, AutoFeatureExtractor
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import re
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import argparse
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import unicodedata
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from typing import Dict
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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 = (
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f"WER: {wer_result}\n"
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f"CER: {cer_result}"
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)
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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 = {
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'ü': 'ue',
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'ö': 'oe',
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'ï': 'i',
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'ë': 'e',
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'ã': 'a',
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'à': 'á',
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'ø': 'o',
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'è': 'é',
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'ê': 'é',
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'å': 'ó',
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'î': 'i',
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'ñ': 'ň',
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'ç': 's',
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'ż': 'ž',
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'ł': 'w',
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'ć': 'č',
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'þ': 't',
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'ß': 'ss',
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'ę': 'en',
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'ą': 'an',
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'æ': 'ae',
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}
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def replace_chars(sentence):
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result = ''
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for ch in sentence:
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new = CHARS[ch] if ch in CHARS else ch
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result += new
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return result
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\/\"\“\„\%\”\<EFBFBD>\–\'\`\«\»\—\’\…\³]'
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text = text.lower()
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# normalize non-standard (stylized) unicode characters
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text = unicodedata.normalize('NFKC', text)
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# remove punctuation
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text = re.sub(chars_to_ignore_regex, "", text)
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text = replace_chars(text)
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# Let's also make sure we split on all kinds of newlines, spaces, etc...
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text = " ".join(text.split())
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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(batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s)
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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", type=str, required=True, 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(
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"--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`"
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)
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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 None. For long audio files a good value would be 5.0 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 None. For long audio files a good value would be 1.0 seconds."
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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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10
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": 0.0,
|
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"processor_class": "Wav2Vec2Processor",
|
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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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pytorch_model.bin
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||||
version https://git-lfs.github.com/spec/v1
|
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oid sha256:721ace680bba9be71df42335fd08f2b631aa72bdd168b1804d9eb98fd9e25c90
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size 1262124593
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1
special_tokens_map.json
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1
special_tokens_map.json
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||||
{"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}]}
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1
tokenizer_config.json
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1
tokenizer_config.json
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|
||||
{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2Processor"}
|
||||
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, "á": 27, "ä": 28, "é": 29, "í": 30, "ó": 31, "ô": 32, "ú": 33, "ý": 34, "č": 35, "ď": 36, "ĺ": 37, "ľ": 38, "ň": 39, "ŕ": 40, "š": 41, "ť": 42, "ž": 43, "̌": 44, "|": 0, "[UNK]": 45, "[PAD]": 46}
|
||||
Reference in New Issue
Block a user