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README.md ADDED
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+ ---
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+ language: ru
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+ datasets:
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+ - common_voice
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+ metrics:
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+ - wer
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+ - cer
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+ tags:
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+ - audio
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+ - automatic-speech-recognition
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+ - speech
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+ - xlsr-fine-tuning-week
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+ license: apache-2.0
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+ model-index:
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+ - name: XLSR Wav2Vec2 Russian by Jonatas Grosman
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+ results:
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+ - task:
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+ name: Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: Common Voice ru
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+ type: common_voice
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+ args: ru
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+ metrics:
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+ - name: Test WER
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+ type: wer
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+ value: 16.79
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+ - name: Test CER
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+ type: cer
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+ value: 40.74
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+
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+ ---
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+
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+ # Wav2Vec2-Large-XLSR-53-Russian
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+
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+ Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Russian using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10).
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+ When using this model, make sure that your speech input is sampled at 16kHz.
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+
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+ The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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+
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+ ## Usage
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+
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+ The model can be used directly (without a language model) as follows:
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+
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+ ```python
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+ import torch
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+ import librosa
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+ from datasets import load_dataset
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+
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+ LANG_ID = "ru"
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+ MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-russian"
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+ SAMPLES = 5
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+
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+ test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
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+
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+ processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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+ model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def speech_file_to_array_fn(batch):
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+ speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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+ batch["speech"] = speech_array
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+ batch["sentence"] = batch["sentence"].upper()
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+ return batch
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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+ inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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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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+
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+ predicted_ids = torch.argmax(logits, dim=-1)
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+ predicted_sentences = processor.batch_decode(predicted_ids)
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+
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+ for i, predicted_sentence in enumerate(predicted_sentences):
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+ print("-" * 100)
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+ print("Reference:", test_dataset[i]["sentence"])
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+ print("Prediction:", predicted_sentence)
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+ ```
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+
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+ | Reference | Prediction |
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+ | ------------- | ------------- |
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+ | ОН РАБОТАТЬ, А ЕЕ НЕ УДЕРЖАТЬ НИКАК — БЕГАЕТ ЗА КЛЁШЕМ КАЖДОГО БУЛЬВАРНИКА. | ОН РАБОТАТЬ А ЕЕ НЕ УДЕРЖАТНИКАК БЕГАЕТ ЗА КЛЕШОМ КАЖДОГО БУЛЬВАРНИКА |
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+ | ЕСЛИ НЕ БУДЕТ ВОЗРАЖЕНИЙ, Я БУДУ СЧИТАТЬ, ЧТО АССАМБЛЕЯ СОГЛАСНА С ЭТИМ ПРЕДЛОЖЕНИЕМ. | ЕСЛИ НЕ БУДЕТ ВОЗРАЖЕНИЙ Я БУДУ СЧИТАТЬ ЧТО АССАМБЛЕЯ СОГЛАСНА С ЭТИМ ПРЕДЛОЖЕНИЕМ |
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+ | ПАЛЕСТИНЦАМ НЕОБХОДИМО СНАЧАЛА УСТАНОВИТЬ МИР С ИЗРАИЛЕМ, А ЗАТЕМ ДОБИВАТЬСЯ ПРИЗНАНИЯ ГОСУДАРСТВЕННОСТИ. | ПАЛЕСТИНЦАМ НЕОБХОДИМО СНАЧАЛА УСТАНОВИТЬ С НИ МИР С ИЗРАИЛЕМ А ЗАТЕМ ДОБИВАТЬСЯ ПРИЗНАНИЯ ГОСУДАРСТВЕННОВСКИЙ |
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+ | У МЕНЯ БЫЛО ТАКОЕ ЧУВСТВО, ЧТО ЧТО-ТО ТАКОЕ ОЧЕНЬ ВАЖНОЕ Я ПРИБАВЛЯЮ. | У МЕНЯ БЫЛО ТАКОЕ ЧУВСТВО ЧТО ЧТО-ТО ТАКОЕ ОЧЕНЬ ВАЖНОЕ Е ПРЕДБАВЛЯЕТ |
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+ | ТОЛЬКО ВРЯД ЛИ ПОЙМЕТ. | ТОЛЬКО ВРЯД ЛИ ПОЙМЕТ |
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+
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+ ## Evaluation
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+
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+ The model can be evaluated as follows on the Russian test data of Common Voice.
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+
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+ ```python
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+ import torch
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+ import re
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+ import librosa
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+ from datasets import load_dataset, load_metric
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+
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+ LANG_ID = "ru"
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+ MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-russian"
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+ DEVICE = "cuda"
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+ MAX_SAMPLES = 8000
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+
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+ CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
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+ "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
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+ "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
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+
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+ test_dataset = load_dataset("common_voice", LANG_ID, split="test")
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+ if len(test_dataset) > MAX_SAMPLES:
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+ test_dataset = test_dataset.select(range(MAX_SAMPLES))
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+
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+ wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
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+ cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
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+
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+ chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
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+
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+ processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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+ model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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+ model.to(DEVICE)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def speech_file_to_array_fn(batch):
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+ batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
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+ speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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+ batch["speech"] = speech_array
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+ return batch
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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def evaluate(batch):
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+ inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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+ with torch.no_grad():
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+ logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
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+
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+ pred_ids = torch.argmax(logits, dim=-1)
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+ batch["pred_strings"] = processor.batch_decode(pred_ids)
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+ return batch
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+
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+ result = test_dataset.map(evaluate, batched=True, batch_size=32)
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+
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+ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"], chunk_size=8000)))
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+ print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"], chunk_size=8000)))
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+ ```
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+
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+ **Test Result**:
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+
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+ - WER: 16.79%
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+ - CER: 40.74%
config.json ADDED
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+ {
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+ "_name_or_path": "facebook/wav2vec2-large-xlsr-53",
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+ "activation_dropout": 0.05,
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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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+ "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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+ "conv_kernel": [
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+ ],
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+ "ctc_loss_reduction": "mean",
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+ "ctc_zero_infinity": true,
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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": 0.05,
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+ "final_dropout": 0.0,
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+ "gradient_checkpointing": true,
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+ "hidden_act": "gelu",
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+ "hidden_dropout": 0.05,
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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": 0.05,
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+ "mask_channel_length": 10,
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+ "mask_channel_min_space": 1,
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+ "mask_channel_other": 0.0,
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+ "mask_channel_prob": 0.0,
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+ "mask_channel_selection": "static",
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+ "mask_feature_length": 10,
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+ "mask_feature_prob": 0.0,
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+ "mask_time_length": 10,
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+ "mask_time_min_space": 1,
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+ "mask_time_other": 0.0,
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+ "mask_time_prob": 0.05,
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+ "mask_time_selection": "static",
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+ "model_type": "wav2vec2",
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+ "num_attention_heads": 16,
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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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+ "pad_token_id": 0,
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+ "transformers_version": "4.5.0.dev0",
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+ "vocab_size": 48
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+ }
preprocessor_config.json ADDED
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+ {
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+ "padding_side": "right",
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+ "padding_value": 0.0,
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+ "return_attention_mask": true,
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+ "sampling_rate": 16000
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+ }
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+ {"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "Е": 6, "С": 7, "О": 8, "O": 9, "Г": 10, "Х": 11, "Щ": 12, "Й": 13, "Ю": 14, "−": 15, "Ь": 16, "X": 17, "Д": 18, "Ё": 19, "К": 20, "У": 21, "А": 22, "З": 23, "I": 24, "Ч": 25, "E": 26, "И": 27, "Ц": 28, "Э": 29, "Ы": 30, "Н": 31, "Ъ": 32, "Ф": 33, "-": 34, "Б": 35, "Л": 36, "C": 37, "Ш": 38, "Я": 39, "Ж": 40, "Т": 41, "М": 42, "'": 43, "В": 44, "П": 45, "Р": 46, "R": 47, "F": 48}