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--- |
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library_name: fairseq |
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task: text-to-speech |
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tags: |
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- fairseq |
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- audio |
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- text-to-speech |
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language: en |
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datasets: |
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- ljspeech |
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--- |
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## Example to download fastspeech2 from fairseq |
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The following should work with fairseq's most up-to-date version in a google colab: |
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```python |
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from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub |
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import IPython.display as ipd |
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import torch |
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model_ensemble, cfg, task = load_model_ensemble_and_task_from_hf_hub( |
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"facebook/fastspeech2-en-ljspeech", arg_overrides={"vocoder": "griffin_lim", "fp16": False} |
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) |
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def tokenize(text): |
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import g2p_en |
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tokenized = g2p_en.G2p()(text) |
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tokenized = [{",": "sp", ";": "sp"}.get(p, p) for p in tokenized] |
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return " ".join(p for p in tokenized if p.isalnum()) |
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text = "Hello, this is a test run." |
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tokenized = tokenize(text) |
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sample = { |
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"net_input": { |
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"src_tokens": task.src_dict.encode_line(tokenized).view(1, -1), |
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"src_lengths": torch.Tensor([len(tokenized.split())]).long(), |
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"prev_output_tokens": None |
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}, |
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"target_lengths": None, |
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"speaker": None, |
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} |
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generator = task.build_generator(model_ensemble, cfg) |
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generation = generator.generate(model_ensemble[0], sample) |
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waveform = generation[0]["waveform"] |
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ipd.Audio(waveform, rate=task.sr) |
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``` |
