readme: ---
  language:
  - es
  datasets:
  - common_voice
  tags:
  - audio
  - automatic-speech-recognition
  ---
  
  # UniSpeech-Large-plus Spanish
  
  [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 Spanish 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-168-es-ft-1h"
  
  sample = next(iter(load_dataset("common_voice", "es", 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:
  # b j e n i k e ɾ ɾ e ɣ a l o a s a β ɾ i ɾ p ɾ i m e ɾ o'
  # for: Bien . ¿ y qué regalo vas a abrir primero ?
  ```
  
  # 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^{+}* - *es*:
  
  ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/unispeech_results.png)