Manu Orsini
d108410d23
## Motivation Human evaluations consistently prefer the English model at temperature **0.3** over the current default **0.7**, and the change is free on every objective axis we measured.
137 lines
3.1 KiB
Python
137 lines
3.1 KiB
Python
"""Configuration models for loading YAML config files."""
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from pathlib import Path
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import yaml
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from pydantic import BaseModel, ConfigDict
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CONFIGS_DIR = Path(__file__).parent.parent / "config"
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class StrictModel(BaseModel):
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model_config = ConfigDict(extra="forbid")
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# Flow configuration
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class FlowConfig(StrictModel):
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dim: int
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depth: int
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# Transformer configuration for FlowLM
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class FlowLMTransformerConfig(StrictModel):
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hidden_scale: int
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max_period: int
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d_model: int
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num_heads: int
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num_layers: int
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class LookupTable(StrictModel):
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dim: int
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n_bins: int
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tokenizer: str
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tokenizer_path: str
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# Root configuration
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class FlowLMConfig(StrictModel):
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"""Root configuration model for YAML config files."""
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dtype: str
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# Nested configurations
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flow: FlowConfig
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transformer: FlowLMTransformerConfig
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# conditioning
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lookup_table: LookupTable
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weights_path: str | None = None
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insert_bos_before_voice: bool = False
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# SEANet configuration
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class SEANetConfig(StrictModel):
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dimension: int
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channels: int
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n_filters: int
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n_residual_layers: int
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ratios: list[int]
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kernel_size: int
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residual_kernel_size: int
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last_kernel_size: int
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dilation_base: int
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pad_mode: str
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compress: int
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# Transformer configuration for Mimi
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class MimiTransformerConfig(StrictModel):
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d_model: int
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input_dimension: int
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output_dimensions: tuple[int, ...]
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num_heads: int
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num_layers: int
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layer_scale: float
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context: int
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max_period: float = 10000.0
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dim_feedforward: int
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# Quantizer configuration
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class QuantizerConfig(StrictModel):
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dimension: int
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output_dimension: int
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# Root configuration
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class MimiConfig(StrictModel):
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"""Root configuration model for Mimi YAML config files."""
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dtype: str
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# Sample rate and channels
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sample_rate: int
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channels: int
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frame_rate: float
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# SEANet configurations
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seanet: SEANetConfig
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# Transformer
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transformer: MimiTransformerConfig
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# Quantizer
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quantizer: QuantizerConfig
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weights_path: str | None = None
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inner_dim: int | None = None
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outer_dim: int | None = None
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class Config(StrictModel):
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flow_lm: FlowLMConfig
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mimi: MimiConfig
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weights_path: str | None = None
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weights_path_without_voice_cloning: str | None = None
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pad_with_spaces_for_short_inputs: bool = False
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remove_semicolons: bool = False
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model_recommended_frames_after_eos: int | None = None
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default_temperature: float = 0.7
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def load_config(yaml_path: str | Path) -> Config:
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yaml_path = Path(yaml_path)
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if not yaml_path.exists():
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if yaml_path.is_relative_to(CONFIGS_DIR):
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raise FileNotFoundError(
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f"Config file not found: {yaml_path}. "
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f"Did you make a typo? Available languages: {[p.stem for p in CONFIGS_DIR.glob('*.yaml')]}"
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)
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raise FileNotFoundError(f"Config file not found: {yaml_path}. Did you make a typo?")
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with open(yaml_path, "r") as f:
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config_dict = yaml.safe_load(f)
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return Config(**config_dict)
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