|
--- |
|
language: en |
|
datasets: |
|
- LIUM/tedlium |
|
tags: |
|
- speech |
|
- audio |
|
- automatic-speech-recognition |
|
--- |
|
Finetuned from [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self). |
|
|
|
# Installation |
|
1. PyTorch installation: https://pytorch.org/ |
|
2. Install transformers: https://huggingface.co/docs/transformers/installation |
|
|
|
e.g., installation by conda |
|
``` |
|
>> conda create -n wav2vec2 python=3.8 |
|
>> conda install pytorch cudatoolkit=11.3 -c pytorch |
|
>> conda install -c conda-forge transformers |
|
``` |
|
|
|
# Usage |
|
```python |
|
# Load the model and processor |
|
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
|
import numpy as np |
|
import torch |
|
|
|
model = Wav2Vec2ForCTC.from_pretrained(r'yongjian/wav2vec2-large-a') # Note: PyTorch Model |
|
processor = Wav2Vec2Processor.from_pretrained(r'yongjian/wav2vec2-large-a') |
|
|
|
# Load input |
|
np_wav = np.random.normal(size=(16000)).clip(-1, 1) # change it to your sample |
|
|
|
# Inference |
|
sample_rate = processor.feature_extractor.sampling_rate |
|
with torch.no_grad(): |
|
model_inputs = processor(np_wav, sampling_rate=sample_rate, return_tensors="pt", padding=True) |
|
logits = model(model_inputs.input_values, attention_mask=model_inputs.attention_mask).logits # use .cuda() for GPU acceleration |
|
pred_ids = torch.argmax(logits, dim=-1).cpu() |
|
pred_text = processor.batch_decode(pred_ids) |
|
print('Transcription:', pred_text) |
|
``` |
|
|
|
# Finetune Code |
|
Github Repo: |
|
https://github.com/CassiniHuy/wav2vec2_finetune |
