pytorch unispeech automatic-speech-recognition fr dataset:common_voice arxiv:2101.07597 transformers audio
---
language:
- fr
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
---

# UniSpeech-Large-plus FRENCH

[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)

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](https://arxiv.org/abs/2101.07597)

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

```python
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](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).

# License

The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)

# Official Results

See *UniSpeeech-L^{+}* - *fr*:

![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/unispeech_results.png)