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--- |
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library_name: transformers |
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language: |
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- ru |
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license: apache-2.0 |
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base_model: jonatasgrosman/wav2vec2-large-xlsr-53-russian |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Wav2vec2-large ru - slowlydoor |
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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 17.0 |
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type: mozilla-foundation/common_voice_17_0 |
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config: ru |
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split: None |
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args: 'config: ru, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 22.3988525667842 |
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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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# Wav2vec2-large ru - slowlydoor ([Automatic Speech Recognition](https://github.com/SlowlyDoor/Automatic-Speech-Recognition)) |
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This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-russian](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-russian) on the Common Voice 17.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2124 |
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- Wer: 22.3989 |
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- Cer: 4.8036 |
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- Ser: 75.4264 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training code |
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```bash |
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pip install datasets librosa scikit-learn torch torchaudio evaluate jiwer nltk |
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pip install --upgrade datasets |
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``` |
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```python |
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from huggingface_hub import login |
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from datasets import load_dataset, DatasetDict |
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from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor, Wav2Vec2ForCTC, TrainingArguments, Trainer |
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from datasets import load_dataset, Audio |
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import torch |
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import torchaudio |
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import re |
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import evaluate |
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import numpy as np |
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from dataclasses import dataclass, field |
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from typing import Any, Dict, List, Optional, Union |
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login("***") |
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common_voice = DatasetDict() |
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common_voice["train"] = load_dataset("mozilla-foundation/common_voice_17_0", "ru", split="train") |
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common_voice["test"] = load_dataset("mozilla-foundation/common_voice_17_0", "ru", split="test") |
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common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"]) |
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common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000)) |
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tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-russian") |
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feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, return_attention_mask=True) |
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processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) |
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def prepare_dataset(batch): |
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audio = batch["audio"] |
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# batched output is "un-batched" |
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batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] |
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batch["input_length"] = len(batch["input_values"]) |
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with processor.as_target_processor(): |
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batch["labels"] = processor(batch["sentence"]).input_ids |
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return batch |
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common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice["train"].column_names, num_proc=2) |
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wer_metric = evaluate.load("wer") |
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cer_metric = evaluate.load("cer") |
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def compute_metrics(pred): |
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pred_logits = pred.predictions |
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pred_ids = np.argmax(pred_logits, axis=-1) |
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pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id |
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pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True) |
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label_str = processor.batch_decode(pred.label_ids, group_tokens=False, skip_special_tokens=True) |
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pairs = [(ref.strip(), hyp.strip()) for ref, hyp in zip(label_str, pred_str)] |
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pairs = [(ref, hyp) for ref, hyp in pairs if len(ref) > 0] |
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if len(pairs) == 0: |
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return {"wer": 1.0, "cer": 1.0, "ser": 1.0} |
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label_str, pred_str = zip(*pairs) |
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wer = 100 * wer_metric.compute(predictions=pred_str, references=label_str) |
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cer = 100 * cer_metric.compute(predictions=pred_str, references=label_str) |
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incorrect_sentences = sum([ref != pred for ref, pred in zip(label_str, pred_str)]) |
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ser = 100 * incorrect_sentences / len(label_str) |
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return { |
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"wer": wer, |
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"cer": cer, |
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"ser": ser |
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} |
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model = Wav2Vec2ForCTC.from_pretrained( |
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"jonatasgrosman/wav2vec2-large-xlsr-53-russian", |
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ctc_loss_reduction="mean", |
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pad_token_id=processor.tokenizer.pad_token_id, |
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) |
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@dataclass |
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class DataCollatorCTCWithPadding: |
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processor: Wav2Vec2Processor |
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padding: Union[bool, str] = True |
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max_length: Optional[int] = None |
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max_length_labels: Optional[int] = None |
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pad_to_multiple_of: Optional[int] = None |
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pad_to_multiple_of_labels: Optional[int] = None |
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def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: |
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# split inputs and labels since they have to be of different lengths and need |
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# different padding methods |
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input_features = [{"input_values": feature["input_values"]} for feature in features] |
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label_features = [{"input_ids": feature["labels"]} for feature in features] |
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batch = self.processor.pad( |
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input_features, |
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padding=self.padding, |
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max_length=self.max_length, |
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pad_to_multiple_of=self.pad_to_multiple_of, |
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return_tensors="pt", |
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) |
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with self.processor.as_target_processor(): |
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labels_batch = self.processor.pad( |
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label_features, |
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padding=self.padding, |
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max_length=self.max_length_labels, |
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pad_to_multiple_of=self.pad_to_multiple_of_labels, |
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return_tensors="pt", |
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) |
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# replace padding with -100 to ignore loss correctly |
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labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) |
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batch["labels"] = labels |
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return batch |
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data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) |
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training_args = TrainingArguments( |
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output_dir="/content/drive/MyDrive/models/wav2vec2-large-ru-5ep", |
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logging_dir="/content/drive/MyDrive/models/wav2vec2-large-ru-5ep", |
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group_by_length=True, |
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per_device_train_batch_size=8, |
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per_device_eval_batch_size=4, |
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eval_strategy="steps", |
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logging_strategy="steps", |
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save_strategy="steps", |
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num_train_epochs=5, |
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logging_steps=25, |
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eval_steps=500, |
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save_steps=500, |
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fp16=True, |
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optim="adamw_torch_fused", |
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torch_compile=True, |
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gradient_checkpointing=True, |
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learning_rate=1e-4, |
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weight_decay=0.005, |
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report_to=["tensorboard"], |
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push_to_hub=False |
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) |
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trainer = Trainer( |
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model=model, |
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data_collator=data_collator, |
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args=training_args, |
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compute_metrics=compute_metrics, |
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train_dataset=common_voice["train"], |
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eval_dataset=common_voice["test"], |
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tokenizer=processor, |
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) |
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trainer.train() |
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``` |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Ser | |
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|:-------------:|:------:|:-----:|:---------------:|:-------:|:------:|:-------:| |
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| 0.3421 | 0.1516 | 500 | 0.2593 | 27.7416 | 6.2518 | 81.6311 | |
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| 0.2979 | 0.3032 | 1000 | 0.2741 | 27.9854 | 6.3745 | 82.2290 | |
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| 0.2787 | 0.4548 | 1500 | 0.2538 | 27.3041 | 6.0743 | 81.1998 | |
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| 0.325 | 0.6064 | 2000 | 0.2701 | 29.4006 | 6.5501 | 83.6503 | |
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| 0.3048 | 0.7580 | 2500 | 0.2435 | 27.0914 | 6.0148 | 80.8077 | |
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| 0.294 | 0.9096 | 3000 | 0.2495 | 26.9503 | 5.9946 | 80.9939 | |
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| 0.2648 | 1.0612 | 3500 | 0.2675 | 26.8356 | 6.0261 | 80.8175 | |
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| 0.2691 | 1.2129 | 4000 | 0.2372 | 26.1220 | 5.8259 | 80.2294 | |
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| 0.2245 | 1.3645 | 4500 | 0.2394 | 26.1603 | 5.8315 | 80.3470 | |
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| 0.2738 | 1.5161 | 5000 | 0.2388 | 26.0420 | 5.7826 | 79.9941 | |
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| 0.2767 | 1.6677 | 5500 | 0.2330 | 25.8089 | 5.7248 | 79.5138 | |
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| 0.2689 | 1.8193 | 6000 | 0.2284 | 25.7312 | 5.6832 | 79.6216 | |
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| 0.2571 | 1.9709 | 6500 | 0.2370 | 25.3403 | 5.6065 | 79.3080 | |
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| 0.2479 | 2.1225 | 7000 | 0.2372 | 25.2065 | 5.5776 | 78.9943 | |
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| 0.2021 | 2.2741 | 7500 | 0.2284 | 24.8718 | 5.4638 | 78.6610 | |
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| 0.1864 | 2.4257 | 8000 | 0.2280 | 24.8132 | 5.4340 | 78.8669 | |
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| 0.1953 | 2.5773 | 8500 | 0.2237 | 24.4941 | 5.3856 | 78.3670 | |
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| 0.195 | 2.7289 | 9000 | 0.2190 | 24.2658 | 5.2770 | 77.8279 | |
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| 0.1829 | 2.8805 | 9500 | 0.2194 | 24.2443 | 5.2697 | 77.8671 | |
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| 0.1457 | 3.0321 | 10000 | 0.2205 | 24.2587 | 5.2398 | 77.8279 | |
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| 0.1435 | 3.1837 | 10500 | 0.2223 | 23.7985 | 5.1608 | 77.1613 | |
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| 0.1435 | 3.3354 | 11000 | 0.2219 | 23.6551 | 5.1230 | 76.9065 | |
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| 0.1752 | 3.4870 | 11500 | 0.2186 | 23.4829 | 5.0767 | 76.5438 | |
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| 0.1793 | 3.6386 | 12000 | 0.2232 | 23.4339 | 5.0977 | 76.4556 | |
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| 0.1682 | 3.7902 | 12500 | 0.2133 | 23.1853 | 5.0090 | 76.0929 | |
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| 0.1607 | 3.9418 | 13000 | 0.2135 | 22.7610 | 4.9091 | 75.7597 | |
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| 0.1463 | 4.0934 | 13500 | 0.2138 | 22.8495 | 4.9314 | 76.1125 | |
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| 0.1654 | 4.2450 | 14000 | 0.2138 | 22.6379 | 4.8814 | 75.7008 | |
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| 0.1586 | 4.3966 | 14500 | 0.2173 | 22.6678 | 4.8705 | 75.5342 | |
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| 0.1438 | 4.5482 | 15000 | 0.2166 | 22.5411 | 4.8437 | 75.5342 | |
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| 0.1645 | 4.6998 | 15500 | 0.2146 | 22.4658 | 4.8308 | 75.3774 | |
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| 0.1254 | 4.8514 | 16000 | 0.2124 | 22.3989 | 4.8036 | 75.4264 | |
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### Framework versions |
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- Transformers 4.52.2 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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