Create README.md

README.md

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+---
+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)
+```
+
+# 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)