|
--- |
|
license: cc-by-4.0 |
|
language: |
|
- qu |
|
metrics: |
|
- cer |
|
- wer |
|
pipeline_tag: automatic-speech-recognition |
|
datasets: |
|
- ivangtorre/second_americas_nlp_2022 |
|
--- |
|
|
|
## Usage |
|
|
|
The model can be used directly (without a language model) as follows: |
|
|
|
```python |
|
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
|
import torch |
|
import torchaudio |
|
|
|
# load model and processor |
|
processor = Wav2Vec2Processor.from_pretrained("ivangtorre/wav2vec2-xlsr-300m-quechua") |
|
model = Wav2Vec2ForCTC.from_pretrained("ivangtorre/wav2vec2-xlsr-300m-quechua") |
|
|
|
# load dummy dataset and read soundfiles |
|
file = torchaudio.load("quechua000573.wav") |
|
|
|
# retrieve logits |
|
logits = model(file[0]).logits |
|
|
|
# take argmax and decode |
|
predicted_ids = torch.argmax(logits, dim=-1) |
|
transcription = processor.batch_decode(predicted_ids) |
|
print("HF prediction: ", transcription) |
|
``` |
|
|
|
|
|
This code snipnet shows how to Evaluate the wav2vec2-xlsr-300m-quechua in [Second Americas NLP 2022 Quechua dev set](https://huggingface.co/datasets/ivangtorre/second_americas_nlp_2022) |
|
|
|
```python |
|
from datasets import load_dataset |
|
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
|
import torch |
|
from jiwer import cer |
|
import torch.nn.functional as F |
|
|
|
|
|
librispeech_eval = load_dataset("ivangtorre/second_americas_nlp_2022", split="validation") |
|
|
|
model = Wav2Vec2ForCTC.from_pretrained("ivangtorre/wav2vec2-xlsr-300m-quechua") |
|
processor = Wav2Vec2Processor.from_pretrained("ivangtorre/wav2vec2-xlsr-300m-quechua") |
|
|
|
def map_to_pred(batch): |
|
wav = batch["audio"][0]["array"] |
|
feats = torch.from_numpy(wav).float() |
|
feats = F.layer_norm(feats, feats.shape) # Normalization performed during finetuning |
|
feats = torch.unsqueeze(feats, 0) |
|
logits = model(feats).logits |
|
predicted_ids = torch.argmax(logits, dim=-1) |
|
batch["transcription"] = processor.batch_decode(predicted_ids) |
|
return batch |
|
|
|
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1) |
|
|
|
print("CER:", cer(result["source_processed"], result["transcription"])) |
|
``` |
|
|
|
## Citation |
|
|
|
```bibtex |
|
@misc{grosman2021xlsr-1b-russian, |
|
title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {R}ussian}, |
|
author={Grosman, Jonatas}, |
|
howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-russian}}, |
|
year={2022} |
|
} |
|
``` |