README.md · jonatasgrosman/wav2vec2-large-xlsr-53-russian at e4ab2bd7dfd0833f4e8252e50d520124f581a19d

wav2vec2-large-xlsr-53-russian / README.md

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	---
	language: ru
	datasets:
	- common_voice
	metrics:
	- wer
	- cer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: XLSR Wav2Vec2 Russian by Jonatas Grosman
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice ru
	type: common_voice
	args: ru
	metrics:
	- name: Test WER
	type: wer
	value: 16.79
	- name: Test CER
	type: cer
	value: 3.68

	---

	# Wav2Vec2-Large-XLSR-53-Russian

	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).
	When using this model, make sure that your speech input is sampled at 16kHz.

	The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

	## Usage

	The model can be used directly (without a language model) as follows:

	```python
	import torch
	import librosa
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

	LANG_ID = "ru"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-russian"
	SAMPLES = 5

	test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

	processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
	model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
	batch["speech"] = speech_array
	batch["sentence"] = batch["sentence"].upper()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)
	inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

	with torch.no_grad():
	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

	predicted_ids = torch.argmax(logits, dim=-1)
	predicted_sentences = processor.batch_decode(predicted_ids)

	for i, predicted_sentence in enumerate(predicted_sentences):
	print("-" * 100)
	print("Reference:", test_dataset[i]["sentence"])
	print("Prediction:", predicted_sentence)
	```

	\| Reference \| Prediction \|
	\| ------------- \| ------------- \|
	\| ОН РАБОТАТЬ, А ЕЕ НЕ УДЕРЖАТЬ НИКАК — БЕГАЕТ ЗА КЛЁШЕМ КАЖДОГО БУЛЬВАРНИКА. \| ОН РАБОТАТЬ А ЕЕ НЕ УДЕРЖАТНИКАК БЕГАЕТ ЗА КЛЕШОМ КАЖДОГО БУЛЬВАРНИКА \|
	\| 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 \|
	\| 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 \|
	\| 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 \|
	\| ТОЛЬКО ВРЯД ЛИ ПОЙМЕТ. \| ТОЛЬКО ВРЯД ЛИ ПОЙМЕТ \|

	## Evaluation

	The model can be evaluated as follows on the Russian test data of Common Voice.

	```python
	import torch
	import re
	import librosa
	from datasets import load_dataset, load_metric
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

	LANG_ID = "ru"
	MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-russian"
	DEVICE = "cuda"

	CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
	"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
	"=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]

	test_dataset = load_dataset("common_voice", LANG_ID, split="test")

	wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
	cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py

	chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"

	processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
	model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
	model.to(DEVICE)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def speech_file_to_array_fn(batch):
	with warnings.catch_warnings():
	warnings.simplefilter("ignore")
	speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
	batch["speech"] = speech_array
	batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
	return batch

	test_dataset = test_dataset.map(speech_file_to_array_fn)

	# Preprocessing the datasets.
	# We need to read the audio files as arrays
	def evaluate(batch):
	\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

	\twith torch.no_grad():
	\t\tlogits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits

	\tpred_ids = torch.argmax(logits, dim=-1)
	\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
	\treturn batch

	result = test_dataset.map(evaluate, batched=True, batch_size=8)

	predictions = [x.upper() for x in result["pred_strings"]]
	references = [x.upper() for x in result["sentence"]]

	print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
	print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
	```

	Test Result:

	My model may report better scores than others because of some specificity of my evaluation script, so I ran the same evaluation script on other models (on 2021-04-22) to make a fairer comparison.

	\| Model \| WER \| CER \|
	\| ------------- \| ------------- \| ------------- \|
	\| jonatasgrosman/wav2vec2-large-xlsr-53-russian \| 16.79% \| 3.68% \|
	\| anton-l/wav2vec2-large-xlsr-53-russian \| 19.49% \| 4.15% \|