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---
license: mit
language: fr
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- per
tags:
- audio
- automatic-speech-recognition
- speech
- phonemize
model-index:
- name: Wav2Vec2-base French finetuned for phonemes by LMSSC
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice v13
type: mozilla-foundation/common_voice_13_0
args: fr
metrics:
- name: Test PER on Common Voice FR 13.0 | Trained
type: per
value: 5.52
- name: Test PER on Multilingual Librispeech FR | Trained
type: per
value: 4.36
- name: Val PER on Common Voice FR 13.0 | Trained
type: per
value: 4.31
---
# Fine-tuned French Voxpopuli v2 wav2vec2-base model for speech-to-phoneme task in French
Fine-tuned [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2) for **French speech-to-phoneme** (without language model) using the train and validation splits of [Common Voice v13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0).
## Audio samplerate for usage
When using this model, make sure that your speech input is **sampled at 16kHz**.
## Training procedure
The model has been finetuned on Coommonvoice-v13 (FR) for 14 epochs on 4x2080 Ti GPUs using a ddp strategy and gradient-accumulation procedure (256 audios per update, corresponding roughly to 25 minutes of speech per update -> 2k updates per epoch)
- Learning rate schedule : Double Tri-state schedule
- Warmup from 1e-5 for 7% of total updates
- Constant at 1e-4 for 28% of total updates
- Linear decrease to 1e-6 for 36% of total updates
- Second warmup boost to 3e-5 for 3% of total updates
- Constant at 3e-5 for 12% of total updates
- Linear decrease to 1e-7 for remaining 14% of updates
- The set of hyperparameters used for training are the same as those detailed in Annex B and Table 6 of [wav2vec2 paper](https://arxiv.org/pdf/2006.11477.pdf).
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