metadata

language: fr
license: apache-2.0
datasets:
  - common_voice
  - mozilla-foundation/common_voice_6_0
metrics:
  - wer
  - cer
tags:
  - fr
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  - robust-speech-event
  - mozilla-foundation/common_voice_6_0
model-index:
  - name: XLSR Wav2Vec2 French by Jonatas Grosman
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice fr
          type: common_voice
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 17.65
          - name: Test CER
            type: cer
            value: 4.89
          - name: Test WER (+LM)
            type: wer
            value: 13.59
          - name: Test CER (+LM)
            type: cer
            value: 3.91
      - 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: Dev WER
            type: wer
            value: 34.35
          - name: Dev CER
            type: cer
            value: 14.09
          - name: Dev WER (+LM)
            type: wer
            value: 24.72
          - name: Dev CER (+LM)
            type: cer
            value: 12.33

Wav2Vec2-Large-XLSR-53-French

Fine-tuned facebook/wav2vec2-large-xlsr-53 on French using the Common Voice. When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :)

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

Usage

The model can be used directly (without a language model) as follows...

Using the ASRecognition library:

from asrecognition import ASREngine

asr = ASREngine("fr", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-french")

audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)

Writing your own inference script:

import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "fr"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-french"
SAMPLES = 10

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)

Reference	Prediction
"CE DERNIER A ÉVOLUÉ TOUT AU LONG DE L'HISTOIRE ROMAINE."	CE DERNIER ÉVOLUÉ TOUT AU LONG DE L'HISTOIRE ROMAINE
CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNASTIE ACHÉMÉNIDE ET SEPT DES SASSANIDES.	CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNASTIE ASHEMÉNID ET SEPT DES SASANDNIDES
"J'AI DIT QUE LES ACTEURS DE BOIS AVAIENT, SELON MOI, BEAUCOUP D'AVANTAGES SUR LES AUTRES."	JAI DIT QUE LES ACTEURS DE BOIS AVAIENT SELON MOI BEAUCOUP DAVANTAGES SUR LES AUTRES
LES PAYS-BAS ONT REMPORTÉ TOUTES LES ÉDITIONS.	LE PAYS-BAS ON REMPORTÉ TOUTES LES ÉDITIONS
IL Y A MAINTENANT UNE GARE ROUTIÈRE.	IL AMNARDIGAD LE TIRAN
HUIT	HUIT
DANS L’ATTENTE DU LENDEMAIN, ILS NE POUVAIENT SE DÉFENDRE D’UNE VIVE ÉMOTION	DANS L'ATTENTE DU LENDEMAIN IL NE POUVAIT SE DÉFENDRE DUNE VIVE ÉMOTION
LA PREMIÈRE SAISON EST COMPOSÉE DE DOUZE ÉPISODES.	LA PREMIÈRE SAISON EST COMPOSÉE DE DOUZE ÉPISODES
ELLE SE TROUVE ÉGALEMENT DANS LES ÎLES BRITANNIQUES.	ELLE SE TROUVE ÉGALEMENT DANS LES ÎLES BRITANNIQUES
ZÉRO	ZEGO

Evaluation

To evaluate on mozilla-foundation/common_voice_6_0 with split test

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-french --dataset mozilla-foundation/common_voice_6_0 --config fr --split test

To evaluate on speech-recognition-community-v2/dev_data

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-french --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0