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https://github.com/serp-ai/bark-with-voice-clone.git
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80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
from typing import Optional
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import numpy as np
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from .generation import codec_decode, generate_coarse, generate_fine, generate_text_semantic
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def text_to_semantic(
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text: str,
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history_prompt: Optional[str] = None,
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temp: float = 0.7,
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):
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"""Generate semantic array from text.
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Args:
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text: text to be turned into audio
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history_prompt: history choice for audio cloning
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temp: generation temperature (1.0 more diverse, 0.0 more conservative)
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Returns:
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numpy semantic array to be fed into `semantic_to_waveform`
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"""
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x_semantic = generate_text_semantic(
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text,
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history_prompt=history_prompt,
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temp=temp,
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)
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return x_semantic
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def semantic_to_waveform(
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semantic_tokens: np.ndarray,
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history_prompt: Optional[str] = None,
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temp: float = 0.7,
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):
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"""Generate audio array from semantic input.
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Args:
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semantic_tokens: semantic token output from `text_to_semantic`
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history_prompt: history choice for audio cloning
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temp: generation temperature (1.0 more diverse, 0.0 more conservative)
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Returns:
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numpy audio array at sample frequency 24khz
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"""
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x_coarse_gen = generate_coarse(
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semantic_tokens,
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history_prompt=history_prompt,
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temp=temp,
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)
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x_fine_gen = generate_fine(
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x_coarse_gen,
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history_prompt=history_prompt,
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temp=0.5,
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)
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audio_arr = codec_decode(x_fine_gen)
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return audio_arr
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def generate_audio(
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text: str,
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history_prompt: Optional[str] = None,
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text_temp: float = 0.7,
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waveform_temp: float = 0.7,
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):
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"""Generate audio array from input text.
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Args:
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text: text to be turned into audio
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history_prompt: history choice for audio cloning
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text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
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waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
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Returns:
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numpy audio array at sample frequency 24khz
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"""
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x_semantic = text_to_semantic(text, history_prompt=history_prompt, temp=text_temp)
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audio_arr = semantic_to_waveform(x_semantic, history_prompt=history_prompt, temp=waveform_temp)
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return audio_arr
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