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--- |
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language: |
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- es |
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datasets: |
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- common_voice |
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tags: |
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- audio |
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- automatic-speech-recognition |
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--- |
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# UniSpeech-Large-plus Spanish |
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[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) |
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The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Spanish phonemes. |
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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. |
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[Paper: UniSpeech: Unified Speech Representation Learning |
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with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) |
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Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang |
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**Abstract** |
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*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.* |
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The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech. |
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# Usage |
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This is an speech model that has been fine-tuned on phoneme classification. |
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## Inference |
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```python |
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import torch |
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from datasets import load_dataset |
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from transformers import AutoModelForCTC, AutoProcessor |
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import torchaudio.functional as F |
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model_id = "microsoft/unispeech-1350-en-168-es-ft-1h" |
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sample = next(iter(load_dataset("common_voice", "es", split="test", streaming=True))) |
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() |
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model = AutoModelForCTC.from_pretrained(model_id) |
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processor = AutoProcessor.from_pretrained(model_id) |
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input_values = processor(resampled_audio, return_tensors="pt").input_values |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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prediction_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(prediction_ids) |
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# -> gives: |
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# 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' |
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# for: Bien . ¿ y qué regalo vas a abrir primero ? |
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``` |
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# Contribution |
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The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten). |
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# License |
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The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) |
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# Official Results |
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See *UniSpeeech-L^{+}* - *es*: |
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