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README.md
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datasets:
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- ljspeech
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---
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## Example
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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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```
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datasets:
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- ljspeech
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---
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## Example
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