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# wav2vec 2.0 with CTC trained on MEDIA

This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on MEDIA (French Language) within
SpeechBrain. Its original SpeechBrain recipe follows the paper of 
G. Laperrière, V. Pelloin, A. Caubriere, S. Mdhaffar, 
N. Camelin, S. Ghannay, B. Jabaian, Y. Estève, 
[The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools](https://aclanthology.org/2022.lrec-1.171).
Find more about the MEDIA corpus within the 
[Media ASR (ELRA-S0272)](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/) and 
[Media SLU (ELRA-E0024)](https://catalogue.elra.info/en-us/repository/browse/ELRA-E0024/) resources.
For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).

The performance of the model is the following:

| Release | Test CER | GPUs |
|:-------------:|:--------------:|:--------:|
| 22-02-23 | 4.78 | 1xV100 32GB |

## Pipeline description

This ASR system is composed of an acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([LeBenchmark/wav2vec2-FR-3K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-large)) is combined with three DNN layers and finetuned on MEDIA.
The obtained final acoustic representation is given to the CTC greedy decoder.

The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.

## Install SpeechBrain

First of all, please install tranformers and SpeechBrain with the following command:

```
pip install speechbrain transformers
```

Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).

### Transcribing your own audio files (in French)

```python
from speechbrain.pretrained import EncoderASR

asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-ctc-MEDIA", savedir="pretrained_models/asr-wav2vec2-ctc-MEDIA")
asr_model.transcribe_file('speechbrain/asr-wav2vec2-ctc-MEDIA/example-fr.wav')

```
### Inference on GPU
To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.

### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```bash
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Download MEDIA related files:
- [Media ASR (ELRA-S0272)](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/)
- [Media SLU (ELRA-E0024)](https://catalogue.elra.info/en-us/repository/browse/ELRA-E0024/)
- [channels.csv and concepts_full_relax.csv](https://drive.google.com/drive/u/1/folders/1z2zFZp3c0NYLFaUhhghhBakGcFdXVRyf)
4. Modify placeholders in hparams/train_hf_wav2vec.yaml:
```bash
data_folder = !PLACEHOLDER
channels_path = !PLACEHOLDER
concepts_path = !PLACEHOLDER
```
5. Run Training:
```bash
cd recipes/MEDIA/ASR/CTC/
python train_hf_wav2vec.py hparams/train_hf_wav2vec.yaml
```

You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1qJUKxsTKrYwzKz0LHzq67M4G06Mj-9fl?usp=sharing).

### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

#### Referencing SpeechBrain

```
@misc{SB2021,
    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
  }
```

#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: https://speechbrain.github.io/

GitHub: https://github.com/speechbrain/speechbrain

{"language": ["fr"], "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "hf-asr-leaderboard"], "datasets": ["MEDIA"], "metrics": ["cer"], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "asr-wav2vec2-ctc-MEDIA", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "MEDIA", "type": "MEDIA_asr", "config": "fr", "split": "test", "args": {"language": "fr"}}, "metrics": [{"type": "cer", "value": 4.78, "name": "Test CER"}]}]}]}
speechbrain wav2vec2 CTC pytorch hf-asr-leaderboard automatic-speech-recognition fr dataset:MEDIA license:apache-2.0 model-index region:us
TAGS
#speechbrain #wav2vec2 #CTC #pytorch #hf-asr-leaderboard #automatic-speech-recognition #fr #dataset-MEDIA #license-apache-2.0 #model-index #region-us 


  
  

wav2vec 2.0 with CTC trained on MEDIA
=====================================


This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on MEDIA (French Language) within
SpeechBrain. Its original SpeechBrain recipe follows the paper of
G. Laperrière, V. Pelloin, A. Caubriere, S. Mdhaffar,
N. Camelin, S. Ghannay, B. Jabaian, Y. Estève,
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools.
Find more about the MEDIA corpus within the
Media ASR (ELRA-S0272) and
Media SLU (ELRA-E0024) resources.
For a better experience, we encourage you to learn more about
SpeechBrain.


The performance of the model is the following:



Pipeline description
--------------------


This ASR system is composed of an acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model (LeBenchmark/wav2vec2-FR-3K-large) is combined with three DNN layers and finetuned on MEDIA.
The obtained final acoustic representation is given to the CTC greedy decoder.


The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe\_file* if needed.


Install SpeechBrain
-------------------


First of all, please install tranformers and SpeechBrain with the following command:


Please notice that we encourage you to read our tutorials and learn more about
SpeechBrain.


### Transcribing your own audio files (in French)


### Inference on GPU


To perform inference on the GPU, add 'run\_opts={"device":"cuda"}' when calling the 'from\_hparams' method.


### Training


The model was trained with SpeechBrain.
To train it from scratch follow these steps:


1. Clone SpeechBrain:
2. Install it:
3. Download MEDIA related files:


* Media ASR (ELRA-S0272)
* Media SLU (ELRA-E0024)
* URL and concepts\_full\_relax.csv


4. Modify placeholders in hparams/train\_hf\_wav2vec.yaml:
5. Run Training:


You can find our training results (models, logs, etc) here.


### Limitations


The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.


#### Referencing SpeechBrain


#### About SpeechBrain


SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.


Website: URL


GitHub: URL



### Transcribing your own audio files (in French) ### Inference on GPU


To perform inference on the GPU, add 'run\_opts={"device":"cuda"}' when calling the 'from\_hparams' method. ### Training


The model was trained with SpeechBrain.
To train it from scratch follow these steps:


1. Clone SpeechBrain:
2. Install it:
3. Download MEDIA related files:


* Media ASR (ELRA-S0272)
* Media SLU (ELRA-E0024)
* URL and concepts\_full\_relax.csv


4. Modify placeholders in hparams/train\_hf\_wav2vec.yaml:
5. Run Training:


You can find our training results (models, logs, etc) here. ### Limitations


The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain #### About SpeechBrain


SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.


Website: URL


GitHub: URL
TAGS
#speechbrain #wav2vec2 #CTC #pytorch #hf-asr-leaderboard #automatic-speech-recognition #fr #dataset-MEDIA #license-apache-2.0 #model-index #region-us 
 ### Transcribing your own audio files (in French) ### Inference on GPU


To perform inference on the GPU, add 'run\_opts={"device":"cuda"}' when calling the 'from\_hparams' method. ### Training


The model was trained with SpeechBrain.
To train it from scratch follow these steps:


1. Clone SpeechBrain:
2. Install it:
3. Download MEDIA related files:


* Media ASR (ELRA-S0272)
* Media SLU (ELRA-E0024)
* URL and concepts\_full\_relax.csv


4. Modify placeholders in hparams/train\_hf\_wav2vec.yaml:
5. Run Training:


You can find our training results (models, logs, etc) here. ### Limitations


The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing SpeechBrain #### About SpeechBrain


SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.


Website: URL


GitHub: URL
TAGS
#speechbrain #wav2vec2 #CTC #pytorch #hf-asr-leaderboard #automatic-speech-recognition #fr #dataset-MEDIA #license-apache-2.0 #model-index #region-us 
### Transcribing your own audio files (in French)### Inference on GPU


To perform inference on the GPU, add 'run\_opts={"device":"cuda"}' when calling the 'from\_hparams' method.### Training


The model was trained with SpeechBrain.
To train it from scratch follow these steps:


1. Clone SpeechBrain:
2. Install it:
3. Download MEDIA related files:


* Media ASR (ELRA-S0272)
* Media SLU (ELRA-E0024)
* URL and concepts\_full\_relax.csv


4. Modify placeholders in hparams/train\_hf\_wav2vec.yaml:
5. Run Training:


You can find our training results (models, logs, etc) here.### Limitations


The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.#### Referencing SpeechBrain#### About SpeechBrain


SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.


Website: URL


GitHub: URL