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

## Usage

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

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

test_dataset = load_dataset("common_voice", "ru", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], 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)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation

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


```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# Download the raw data instead of using HF datasets to save disk space 
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ru.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()

wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
model.to("cuda")

cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ru/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/ru/clips/"

def clean_sentence(sent):
    sent = sent.lower()
    # these letters are considered equivalent in written Russian
    sent = sent.replace('ё', 'е')
    # replace non-alpha characters with space
    sent = "".join(ch if ch.isalpha() else " " for ch in sent)
    # remove repeated spaces
    sent = " ".join(sent.split())
    return sent

targets = []
preds = []

for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
    row["sentence"] = clean_sentence(row["sentence"])
    speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
    row["speech"] = resampler(speech_array).squeeze().numpy()

    inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

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

    pred_ids = torch.argmax(logits, dim=-1)

    targets.append(row["sentence"])
    preds.append(processor.batch_decode(pred_ids)[0])

# free up some memory
del model
del processor
del cv_test

print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```

**Test Result**: 17.39 %  


## Training

The Common Voice `train` and `validation` datasets were used for training.

{"language": "ru", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Russian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ru", "type": "common_voice", "args": "ru"}, "metrics": [{"type": "wer", "value": 17.39, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week ru dataset:common_voice license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 


# Wav2Vec2-Large-XLSR-53-Russian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

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




## Evaluation

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




Test Result: 17.39 %  


## Training

The Common Voice 'train' and 'validation' datasets were used for training.

# Wav2Vec2-Large-XLSR-53-Russian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

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

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




Test Result: 17.39 % ## Training

The Common Voice 'train' and 'validation' datasets were used for training.
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # Wav2Vec2-Large-XLSR-53-Russian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

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

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




Test Result: 17.39 % ## Training

The Common Voice 'train' and 'validation' datasets were used for training.
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# Wav2Vec2-Large-XLSR-53-Russian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.## Usage

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

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




Test Result: 17.39 %## Training

The Common Voice 'train' and 'validation' datasets were used for training.