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---
license: apache-2.0
language: fr
library_name: transformers
thumbnail: null
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
- CTC
- Wav2vec2
datasets:
- common_voice
- mozilla-foundation/common_voice_11_0
- facebook/multilingual_librispeech
- facebook/voxpopuli
- gigant/african_accented_french
metrics:
- wer
model-index:
- name: Fine-tuned wav2vec2-FR-7K-large model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11
type: mozilla-foundation/common_voice_11_0
args: fr
metrics:
- name: Test WER
type: wer
value: 11.44
- name: Test WER (+LM)
type: wer
value: 9.66
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech (MLS)
type: facebook/multilingual_librispeech
args: french
metrics:
- name: Test WER
type: wer
value: 5.93
- name: Test WER (+LM)
type: wer
value: 5.13
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: VoxPopuli
type: facebook/voxpopuli
args: fr
metrics:
- name: Test WER
type: wer
value: 9.33
- name: Test WER (+LM)
type: wer
value: 8.51
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: African Accented French
type: gigant/african_accented_french
args: fr
metrics:
- name: Test WER
type: wer
value: 16.22
- name: Test WER (+LM)
type: wer
value: 15.39
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 16.56
- name: Test WER (+LM)
type: wer
value: 12.96
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Fleurs
type: google/fleurs
args: fr_fr
metrics:
- name: Test WER
type: wer
value: 10.10
- name: Test WER (+LM)
type: wer
value: 8.84
---
# Fine-tuned wav2vec2-FR-7K-large model for ASR in French
<style>
img {
display: inline;
}
</style>
[](#model-architecture) [](#model-architecture) [](#datasets)
This model is a fine-tuned version of [LeBenchmark/wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large), trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and validation splits of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co/datasets/gigant/african_accented_french). When using the model make sure that your speech input is also sampled at 16Khz.
## Usage
1. To use on a local audio file with the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM
model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").cuda()
processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("bhuang/asr-wav2vec2-french")
wav_path = "example.wav" # path to your audio file
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor_with_lm(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
predicted_sentence = processor_with_lm.batch_decode(logits.cpu().numpy()).text[0]
```
2. To use on a local audio file without the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2Processor
model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").cuda()
processor = Wav2Vec2Processor.from_pretrained("bhuang/asr-wav2vec2-french")
wav_path = "example.wav" # path to your audio file
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
# decode
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentence = processor.batch_decode(predicted_ids)[0]
```
## Evaluation
1. To evaluate on `mozilla-foundation/common_voice_11_0`
```bash
python eval.py \
--model_id "bhuang/asr-wav2vec2-french" \
--dataset "mozilla-foundation/common_voice_11_0" \
--config "fr" \
--split "test" \
--log_outputs \
--outdir "outputs/results_mozilla-foundatio_common_voice_11_0_with_lm"
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py \
--model_id "bhuang/asr-wav2vec2-french" \
--dataset "speech-recognition-community-v2/dev_data" \
--config "fr" \
--split "validation" \
--chunk_length_s 30.0 \
--stride_length_s 5.0 \
--log_outputs \
--outdir "outputs/results_speech-recognition-community-v2_dev_data_with_lm"
```
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