unispeech-1350-en-353-fr-ft-1h

UniSpeech-Large-plus FRENCH

Microsoft's UniSpeech

The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of French phonemes. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes.

Paper: UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang

Abstract In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.

The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech.

Usage

This is an speech model that has been fine-tuned on phoneme classification.

Inference

import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F

model_id = "microsoft/unispeech-1350-en-353-fr-ft-1h"

sample = next(iter(load_dataset("common_voice", "fr", split="test", streaming=True)))
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()

model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)

input_values = processor(resampled_audio, return_tensors="pt").input_values

with torch.no_grad():
    logits = model(input_values).logits

prediction_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(prediction_ids)
# gives -> 'œ̃ v ʁ ɛ t ʁ a v a j ɛ̃ t e ʁ ɛ s ɑ̃ v a ɑ̃ f ɛ̃ ɛ t ʁ ə m ə n e s y ʁ s ə s y ʒ ɛ'
# for 'Un vrai travail intéressant va, enfin, être mené sur ce sujet.'

Contribution

The model was contributed by cywang and patrickvonplaten.

License

The official license can be found here

Official Results

See UniSpeeech-L^{+} - fr: