---
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_15_0
language:
- fr
metrics:
- wer
base_model:
- LeBenchmark/wav2vec2-FR-7K-large
pipeline_tag: automatic-speech-recognition
library_name: speechbrain
tags:
- Transformer
- wav2vec2
- CTC
- inference
---

# asr-wav2vec2-commonvoice-15-fr : LeBenchmark/wav2vec2-FR-7K-large fine-tuned on CommonVoice 15.0 French

<!-- Provide a quick summary of what the model is/does. -->

*asr-wav2vec2-commonvoice-15-fr* is an Automatic Speech Recognition model fine-tuned on CommonVoice 15.0 French set with *LeBenchmark/wav2vec2-FR-7K-large* as the pretrained wav2vec2 model.

The fine-tuned model achieves the following performance :
|:-------------:|:--------------:|:--------------:| :--------:|
| Release | Valid WER | Test WER | GPUs |
|:-------------:|:--------------:|:--------------:| :--------:|
| 2023-09-08 | 9.14  | 11.21  | 4xV100 32GB |
|:-------------:|:--------------:|:--------------:| :--------:|

## Model Details

The ASR system is composed of:
- the **Tokenizer** (char) that transforms the input text into a sequence of characters ("cat" into ["c", "a", "t"]) and trained with the train transcriptions (train.tsv).
- the **Acoustic model** (wav2vec2.0 + DNN + CTC greedy decode). The pretrained wav2vec 2.0 model (LeBenchmark/wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large) is combined with two DNN layers and fine-tuned on CommonVoice FR.
The final acoustic representation is given to the CTC greedy decode.

We used recordings sampled at 16kHz (single channel).

- **Developed by:** Cécile Macaire
- **Funded by [optional]:** GENCI-IDRIS (Grant 2023-AD011013625R1)
PROPICTO ANR-20-CE93-0005
- **Language(s) (NLP):** French
- **License:** Apache-2.0
- **Finetuned from model:** LeBenchmark/wav2vec2-FR-7K-large


## How to Get Started with the Model

## Training Details


### Training Data



### Training Procedure



#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation

```bibtex
@inproceedings{macaire24_interspeech,
  title     = {Towards Speech-to-Pictograms Translation},
  author    = {Cécile Macaire and Chloé Dion and Didier Schwab and Benjamin Lecouteux and Emmanuelle Esperança-Rodier},
  year      = {2024},
  booktitle = {Interspeech 2024},
  pages     = {857--861},
  doi       = {10.21437/Interspeech.2024-490},
  issn      = {2958-1796},
}
```