File size: 4,388 Bytes
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from __future__ import annotations
import io
from typing import Any, ClassVar
import soundfile as sf
from server.schemas import Lang, ParamSpec
from server.seed import apply_seed
class Adapter:
id: ClassVar[str] = "chatterbox-en"
label: ClassVar[str] = "Chatterbox (English)"
description: ClassVar[str] = (
"Original Chatterbox English voice cloning with CFG and exaggeration controls."
)
languages: ClassVar[list[Lang]] = [Lang(code="en", label="English")]
paralinguistic_tags: ClassVar[list[str]] = []
supports_voice_clone: ClassVar[bool] = True
params: ClassVar[list[ParamSpec]] = [
ParamSpec(
name="exaggeration", label="Exaggeration", type="float",
default=0.5, min=0.0, max=2.0, step=0.05,
help="How emotive the speech is. Higher pushes prosody, pacing, and emphasis; lower stays flat and neutral.",
group="basic",
),
ParamSpec(
name="cfg_weight", label="CFG weight", type="float",
default=0.5, min=0.0, max=1.0, step=0.05,
help="Classifier-free guidance. Higher sticks closer to the reference voice; lower allows more variation but may drift in identity.",
group="basic",
),
ParamSpec(
name="temperature", label="Temperature", type="float",
default=0.8, min=0.1, max=1.5, step=0.05,
help="Sampling randomness. Lower = deterministic and safer; higher = more creative but riskier and prone to artifacts.",
group="basic",
),
ParamSpec(
name="seed", label="Seed", type="int",
default=-1, min=-1, step=1,
help="Reproducibility. -1 draws a fresh random seed every run; any non-negative value pins the result so you can reproduce it.",
group="advanced",
),
ParamSpec(
name="repetition_penalty", label="Repetition penalty", type="float",
default=1.2, min=1.0, max=3.0, step=0.05,
help="Discourages repeating the same tokens. >1 reduces stuttering and loops; too high hurts natural fluency.",
group="advanced",
),
ParamSpec(
name="min_p", label="Min p", type="float",
default=0.05, min=0.0, max=1.0, step=0.01,
help="Cuts off tokens whose probability is below this fraction of the top token's. Higher trims more aggressively.",
group="advanced",
),
ParamSpec(
name="top_p", label="Top p", type="float",
default=1.0, min=0.0, max=1.0, step=0.01,
help="Nucleus sampling. Keep tokens until cumulative probability reaches this. Lower = safer/conservative.",
group="advanced",
),
]
def __init__(self, device: str) -> None:
self.device = device
self._model = None
def load(self) -> None:
from chatterbox.tts import ChatterboxTTS
self._model = ChatterboxTTS.from_pretrained(device=self.device)
def unload(self) -> None:
self._model = None
def generate(
self,
text: str,
reference_wav_path: str | None,
language: str | None,
params: dict[str, Any],
) -> tuple[bytes, int, int]:
if self._model is None:
raise RuntimeError("model not loaded")
seed_used = apply_seed(params.get("seed"))
wav = self._model.generate(
text,
audio_prompt_path=reference_wav_path,
exaggeration=float(params.get("exaggeration", 0.5)),
cfg_weight=float(params.get("cfg_weight", 0.5)),
temperature=float(params.get("temperature", 0.8)),
repetition_penalty=float(params.get("repetition_penalty", 1.2)),
min_p=float(params.get("min_p", 0.05)),
top_p=float(params.get("top_p", 1.0)),
)
import numpy as np
import torch
if hasattr(wav, "detach"):
wav = wav.detach().cpu().numpy()
if isinstance(wav, torch.Tensor): # pragma: no cover
wav = wav.numpy()
arr = np.asarray(wav).squeeze()
sr = getattr(self._model, "sr", 24000)
buf = io.BytesIO()
sf.write(buf, arr, sr, format="WAV", subtype="PCM_16")
return buf.getvalue(), sr, seed_used
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