# French speech recognition

French speech recognition model fine-tuned from openai/whisper-large-v2, trained on over 2200 hours of French audio data

A French automatic speech recognition model fine-tuned based on the facebook/wav2vec2-large-fr-voxpopuli model, trained using the Common Voice 7.0 French dataset.

A speech recognition model fine-tuned using the Common Voice 7.0 (French) dataset, based on the microsoft/unispeech-large-1500h-cv model

This is a French speech recognition model fine-tuned based on facebook/wav2vec2-base-960h, achieving a word error rate of 1.0 on the evaluation set.

An automatic speech recognition model fine-tuned on the GOOGLE/XTREME_S - MINDS14.FR-FR dataset based on facebook/wav2vec2-xls-r-300m

UniSpeech is a unified speech representation learning model that combines labeled and unlabeled data for pre-training, specifically fine-tuned for French.

This is an end-to-end automatic speech recognition system trained on the CommonVoice French dataset, utilizing a CRDNN architecture combined with CTC and attention mechanisms.

wav2vec 2.0 speech recognition model trained on the CommonVoice French dataset, using CTC/Attention architecture without requiring a language model

This is a French speech recognition model fine-tuned from the XLSR-53 large model, trained on the Common Voice dataset, supporting high-accuracy French speech-to-text conversion.

This is a French automatic speech recognition model based on the XLS-R 1B architecture, fine-tuned on multiple French speech datasets.

Facebook's Wav2Vec2 base model, pre-trained exclusively on French using 22.8k unlabeled data from the VoxPopuli corpus.

Wav2Vec2 base model pre-trained on unannotated French data from VoxPopuli, suitable for French speech recognition tasks

A French speech recognition model fine-tuned from facebook/wav2vec2-large-fr-voxpopuli, trained on the Common Voice 6.1 French dataset, supporting 16kHz audio input

A French automatic speech recognition model based on the wav2vec2-large-xlsr-53 architecture, supporting punctuation prediction

An automatic speech recognition model fine-tuned on the Common Voice French dataset based on facebook/wav2vec2-large-xlsr-53, with a test WER of 34.86%.

Base wav2vec2 model trained on 2.6K hours of French speech data, excluding spontaneous speech

This is an automatic speech recognition (ASR) model based on the wav2vec2 architecture, specifically fine-tuned for French, achieving a word error rate (WER) of 12.82% on the Common Voice French test set.