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from typing import Dict, Any
import tempfile
import torchaudio
import soundfile as sf
import re
from num2words import num2words
from f5_tts.model import DiT
from f5_tts.infer.utils_infer import (
load_vocoder,
load_model,
preprocess_ref_audio_text,
infer_process,
remove_silence_for_generated_wav,
)
import base64
import io
import numpy as np
from huggingface_hub import hf_hub_download
import traceback
class EndpointHandler:
def __init__(self, path=""):
self.vocoder = load_vocoder()
model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
model_path = hf_hub_download(
repo_id="jpgallegoar/F5-Spanish",
filename="model_1200000.safetensors"
)
self.ema_model = load_model(DiT, model_cfg, model_path)
def traducir_numero_a_texto(self, texto):
texto_separado = re.sub(r'([A-Za-z])(\d)', r'\1 \2', texto)
texto_separado = re.sub(r'(\d)([A-Za-z])', r'\1 \2', texto_separado)
def reemplazar_numero(match):
numero = match.group()
return num2words(int(numero), lang='es')
return re.sub(r'\b\d+\b', reemplazar_numero, texto_separado)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
try:
ref_audio_base64 = data.get("ref_audio")
if not ref_audio_base64:
return {
"success": False,
"error": "Missing required field: 'ref_audio'"
}
# Decode base64 audio and write to temp file
try:
audio_bytes = base64.b64decode(ref_audio_base64)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
temp_audio_file.write(audio_bytes)
temp_audio_path = temp_audio_file.name
except Exception as e:
return {
"success": False,
"error": f"Invalid audio data: {type(e).__name__}: {str(e)}"
}
ref_text = data.get("ref_text", "")
gen_text = data.get("gen_text", "")
if not gen_text:
return {
"success": False,
"error": "Missing required field: 'gen_text'"
}
remove_silence = data.get("remove_silence", True)
cross_fade_duration = data.get("cross_fade_duration", 0.15)
speed = data.get("speed", 1.0)
ref_audio, ref_text = preprocess_ref_audio_text(temp_audio_path, ref_text, show_info=print)
if not gen_text.startswith(" "):
gen_text = " " + gen_text
if not gen_text.endswith(". "):
gen_text += ". "
gen_text = self.traducir_numero_a_texto(gen_text.lower())
final_wave, final_sample_rate, _ = infer_process(
ref_audio,
ref_text,
gen_text,
self.ema_model,
self.vocoder,
cross_fade_duration=cross_fade_duration,
speed=speed,
show_info=print,
)
if remove_silence:
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
sf.write(f.name, final_wave, final_sample_rate)
remove_silence_for_generated_wav(f.name)
final_wave, _ = torchaudio.load(f.name)
final_wave = final_wave.squeeze().cpu().numpy()
with io.BytesIO() as buffer:
sf.write(buffer, final_wave, final_sample_rate, format="WAV")
buffer.seek(0)
encoded_audio = base64.b64encode(buffer.read()).decode("utf-8")
return {
"success": True,
"audio_base64": encoded_audio
}
except Exception as e:
print("==== Exception Traceback ====")
traceback.print_exc()
print("==== End Traceback ====")
return {
"success": False,
"error": f"{type(e).__name__}: {str(e)}"
}
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