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--- |
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language: en |
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datasets: |
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- librispeech |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- asr |
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- hubert |
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license: apache-2.0 |
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metrics: |
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- wer |
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- cer |
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--- |
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# voidful/tts_hubert_cluster_bart_base |
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## Usage |
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````python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("voidful/tts_hubert_cluster_bart_base") |
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model = AutoModelForSeq2SeqLM.from_pretrained("voidful/tts_hubert_cluster_bart_base") |
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```` |
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generate output |
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```python |
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gen_output = model.generate(input_ids=tokenizer("going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to ask immediately afterwards",return_tensors='pt').input_ids, max_length=1024) |
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print(tokenizer.decode(gen_output[0], skip_special_tokens=True)) |
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``` |
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## Result |
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`:vtok402::vtok329::vtok329::vtok75::vtok75::vtok75::vtok44::vtok150::vtok150::vtok222::vtok280::vtok280::vtok138::vtok409::vtok409::vtok409::vtok46::vtok441:` |
