104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
import hashlib
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import logging
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import time
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from pathlib import Path
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import requests
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import safetensors.torch
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import torch
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from huggingface_hub import hf_hub_download
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from torch import nn
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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_voices_names = ["alba", "marius", "javert", "jean", "fantine", "cosette", "eponine", "azelma"]
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PREDEFINED_VOICES = {
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# don't forget to change this
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x: f"hf://kyutai/pocket-tts-without-voice-cloning/embeddings/{x}.safetensors"
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for x in _voices_names
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}
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def make_cache_directory() -> Path:
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cache_dir = Path.home() / ".cache" / "pocket_tts"
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cache_dir.mkdir(parents=True, exist_ok=True)
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return cache_dir
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def print_nb_parameters(model: nn.Module, model_name: str):
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logger = logging.getLogger(__name__)
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state_dict = model.state_dict()
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total = 0
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for key, value in state_dict.items():
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logger.info("%s: %,d", key, value.numel())
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total += value.numel()
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logger.info("Total number of parameters in %s: %,d", model_name, total)
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def size_of_dict(state_dict: dict) -> int:
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total_size = 0
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for value in state_dict.values():
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if isinstance(value, torch.Tensor):
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total_size += value.numel() * value.element_size()
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elif isinstance(value, dict):
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total_size += size_of_dict(value)
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return total_size
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class display_execution_time:
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def __init__(self, task_name: str, print_output: bool = True):
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self.task_name = task_name
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self.print_output = print_output
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self.start_time = None
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self.elapsed_time_ms = None
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self.logger = logging.getLogger(__name__)
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def __enter__(self):
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self.start_time = time.monotonic()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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end_time = time.monotonic()
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self.elapsed_time_ms = int((end_time - self.start_time) * 1000)
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if self.print_output:
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self.logger.info("%s took %d ms", self.task_name, self.elapsed_time_ms)
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return False # Don't suppress exceptions
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def download_if_necessary(file_path: str) -> Path:
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if file_path.startswith("http://") or file_path.startswith("https://"):
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cache_dir = make_cache_directory()
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cached_file = cache_dir / (
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hashlib.sha256(file_path.encode()).hexdigest() + "." + file_path.split(".")[-1]
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)
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if not cached_file.exists():
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response = requests.get(file_path)
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response.raise_for_status()
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with open(cached_file, "wb") as f:
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f.write(response.content)
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return cached_file
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elif file_path.startswith("hf://"):
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file_path = file_path.removeprefix("hf://")
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splitted = file_path.split("/")
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repo_id = "/".join(splitted[:2])
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filename = "/".join(splitted[2:])
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if "@" in filename:
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filename, revision = filename.split("@")
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else:
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revision = None
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cached_file = hf_hub_download(repo_id=repo_id, filename=filename, revision=revision)
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return Path(cached_file)
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else:
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return Path(file_path)
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def load_predefined_voice(voice_name: str) -> torch.Tensor:
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if voice_name not in PREDEFINED_VOICES:
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raise ValueError(
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f"Predefined voice '{voice_name}' not found"
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f", available voices are {list(PREDEFINED_VOICES)}."
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)
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voice_file = download_if_necessary(PREDEFINED_VOICES[voice_name])
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# There is only one tensor in the file.
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return safetensors.torch.load_file(voice_file)["audio_prompt"]
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