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