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58197e1896
* Start sketching parler-tts support. * Implement the attention. * Add the example code. * Fix the example. * Add the description + t5 encode it. * More of the parler forward pass. * Fix the positional embeddings. * Support random sampling in generation. * Handle EOS. * Add the python decoder. * Proper causality mask.
30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
import torch
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import torchaudio
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from safetensors.torch import load_file
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from parler_tts import DACModel
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tensors = load_file("out.safetensors")
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dac_model = DACModel.from_pretrained("parler-tts/dac_44khZ_8kbps")
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output_ids = tensors["codes"][None, None]
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print(output_ids, "\n", output_ids.shape)
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batch_size = 1
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with torch.no_grad():
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output_values = []
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for sample_id in range(batch_size):
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sample = output_ids[:, sample_id]
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sample_mask = (sample >= dac_model.config.codebook_size).sum(dim=(0, 1)) == 0
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if sample_mask.sum() > 0:
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sample = sample[:, :, sample_mask]
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sample = dac_model.decode(sample[None, ...], [None]).audio_values
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output_values.append(sample.transpose(0, 2))
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else:
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output_values.append(torch.zeros((1, 1, 1)).to(dac_model.device))
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output_lengths = [audio.shape[0] for audio in output_values]
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pcm = (
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torch.nn.utils.rnn.pad_sequence(output_values, batch_first=True, padding_value=0)
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.squeeze(-1)
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.squeeze(-1)
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)
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print(pcm.shape, pcm.dtype)
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torchaudio.save("out.wav", pcm.cpu(), sample_rate=44100)
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