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8952fff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | """
F5-TTS voice cloning engine.
Generates speech in a target speaker's voice given a short reference WAV.
Falls back to None gracefully if f5-tts is not installed or the GPU is
unavailable — the caller then falls back to MMS-TTS.
Install:
pip install f5-tts>=1.0.0
Reference:
SWivid/F5-TTS (HuggingFace / GitHub)
Model: ~750 MB, downloaded on first use to HF cache.
"""
from __future__ import annotations
import logging
import threading
from pathlib import Path
from typing import Optional, Tuple
import numpy as np
logger = logging.getLogger(__name__)
_lock = threading.Lock()
_model = None # F5TTS instance, loaded lazily
def _load_model():
global _model
if _model is not None:
return _model
with _lock:
if _model is None:
from f5_tts.api import F5TTS # type: ignore
_model = F5TTS(model_type="F5TTS")
logger.info("F5-TTS model loaded.")
return _model
def synthesize(
text: str,
ref_wav_path: str,
ref_text: str = "",
speed: float = 1.0,
device: str = "cuda",
) -> Optional[Tuple[np.ndarray, int]]:
"""
Generate speech for `text` using `ref_wav_path` as the speaker reference.
Args:
text: Text to synthesize (Bambara, Fula, French, or English).
ref_wav_path: Path to reference audio (WAV, 5–30 s of the target speaker).
ref_text: Transcript of the reference audio. If empty the model
uses in-context inference (slightly lower quality but still
good for voice matching).
speed: Speaking rate multiplier. 1.0 = normal.
device: "cuda" or "cpu". CPU is 30-60 s/sentence — use GPU.
Returns:
(waveform_float32, sample_rate) or None on failure.
"""
if not text.strip():
return None
try:
import torch
model = _load_model()
wav, sr, _ = model.infer(
ref_file=ref_wav_path,
ref_text=ref_text.strip(),
gen_text=text.strip(),
speed=speed,
target_rms=0.1,
cross_fade_duration=0.15,
nfe_step=32,
cfg_strength=2.0,
show_info=False,
progress=None,
)
if isinstance(wav, torch.Tensor):
wav = wav.cpu().float().numpy()
else:
wav = np.asarray(wav, dtype=np.float32)
return wav, int(sr)
except ImportError:
logger.warning(
"f5-tts not installed — voice cloning disabled. "
"Add 'f5-tts>=1.0.0' to requirements.txt."
)
return None
except Exception as exc:
logger.error("F5-TTS synthesis failed: %s", exc)
return None
def to_wav_24k(audio_path: str) -> str:
"""
Resample any audio file to 24 kHz mono WAV (F5-TTS preferred sample rate).
Returns the path to the converted file (same stem, .wav extension).
Modifies in-place if the input is already a WAV — otherwise writes a new file.
"""
import librosa
import soundfile as sf
out_path = str(Path(audio_path).with_suffix(".f5ref.wav"))
audio, _ = librosa.load(audio_path, sr=24_000, mono=True)
sf.write(out_path, audio, 24_000)
return out_path
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