
# Fine-Tune Wav2Vec2 large model for English ASR


### Data for fine-tune

| Dataset      | Duration in hours |
|--------------|-------------------|
| Common Voice |              1667 |
| Europarl     |                85 |
| How2         |               356 |
| Librispeech  |               936 |
| MuST-C v1    |               407 |
| MuST-C v2    |               482 |
| Tedlium      |               482 |


### Evaluation result

| Dataset     | Duration in hours | WER w/o LM | WER with LM |
|-------------|-------------------|------------|-------------|
| Librispeech |        5.4        |     2.9    |     1.1     |
|   Tedlium   |        2.6        |     7.9    |     5.4     |


### Usage

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1FAhtGvjRdHT4W0KeMdMMlL7sm6Hbe7dv?usp=sharing)

```python
from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
from transformers import Wav2Vec2ProcessorWithLM
from IPython.lib.display import Audio
import torchaudio
import torch

# Load model & processor
model_name = "nguyenvulebinh/iwslt-asr-wav2vec-large-4500h"
model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)

# Load an example audio (16k)
audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="tst_2010_sample.wav")))
input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')

# Infer
output = model(**input_data)

# Output transcript without LM
print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
# and of course there's teams that have a lot more tada structures and among the best are recent graduates of kindergarten

# Output transcript with LM
print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
# and of course there are teams that have a lot more ta da structures and among the best are recent graduates of kindergarten
```

### Model Parameters License

The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode


### Contact 

nguyenvulebinh@gmail.com

[![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
{"language": "en", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["common_voice", "librispeech_asr", "how2", "must-c-v1", "must-c-v2", "europarl", "tedlium"]}
transformers pytorch wav2vec2 automatic-speech-recognition audio en dataset:common_voice dataset:librispeech_asr dataset:how2 dataset:must-c-v1 dataset:must-c-v2 dataset:europarl dataset:tedlium license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-common_voice #dataset-librispeech_asr #dataset-how2 #dataset-must-c-v1 #dataset-must-c-v2 #dataset-europarl #dataset-tedlium #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

Fine-Tune Wav2Vec2 large model for English ASR
==============================================


### Data for fine-tune



### Evaluation result



### Usage


![Open In Colab](URL


### Model Parameters License


The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: URL


### Contact


nguyenvulebinh@URL


![Follow](URL



### Data for fine-tune ### Evaluation result ### Usage


![Open In Colab](URL ### Model Parameters License


The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: URL ### Contact


nguyenvulebinh@URL


![Follow](URL
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-common_voice #dataset-librispeech_asr #dataset-how2 #dataset-must-c-v1 #dataset-must-c-v2 #dataset-europarl #dataset-tedlium #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
 ### Data for fine-tune ### Evaluation result ### Usage


![Open In Colab](URL ### Model Parameters License


The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: URL ### Contact


nguyenvulebinh@URL


![Follow](URL
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-common_voice #dataset-librispeech_asr #dataset-how2 #dataset-must-c-v1 #dataset-must-c-v2 #dataset-europarl #dataset-tedlium #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
### Data for fine-tune### Evaluation result### Usage


![Open In Colab](URL### Model Parameters License


The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: URL### Contact


nguyenvulebinh@URL


![Follow](URL