File size: 2,059 Bytes
907758c 84a5dc0 907758c 549fc0c 907758c 549fc0c 907758c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
language: pt
datasets:
- Common Voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- Russian-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Edresson Casanova Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Russian
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test Common Voice 7.0 WER
type: wer
value: 36.59
---
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Russian
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Russian using a single-speaker dataset plus a data augmentation method based on TTS and voice conversion.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resampl(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
|