---
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.

## Model Details



### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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 [optional]:** LeBenchmark/wav2vec2-FR-7K-large

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/macairececile/speech-to-pictograms.
- **Paper [optional]:**


## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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

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[More Information Needed]

#### Metrics

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

[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[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 [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**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},
}