Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,565 Bytes
8b11278 3bd9bc4 8b11278 9fe64e4 8b11278 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
import argparse
import codecs
import os
import re
from importlib.resources import files
from pathlib import Path
import numpy as np
import soundfile as sf
import tomli
from cached_path import cached_path
from infer.utils_infer import (
infer_process,
load_model,
load_vocoder,
preprocess_ref_audio_text,
remove_silence_for_generated_wav,
)
from model import DiT, UNetT
parser = argparse.ArgumentParser(
prog="python3 infer-cli.py",
description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
epilog="Specify options above to override one or more settings from config.",
)
parser.add_argument(
"-c",
"--config",
help="Configuration file. Default=infer/examples/basic/basic.toml",
default=os.path.join(files("f5_tts").joinpath("infer/examples/basic"), "basic.toml"),
)
parser.add_argument(
"-m",
"--model",
help="F5-TTS | E2-TTS",
)
parser.add_argument(
"-p",
"--ckpt_file",
help="The Checkpoint .pt",
)
parser.add_argument(
"-v",
"--vocab_file",
help="The vocab .txt",
)
parser.add_argument("-r", "--ref_audio", type=str, help="Reference audio file < 15 seconds.")
parser.add_argument("-s", "--ref_text", type=str, default="666", help="Subtitle for the reference audio.")
parser.add_argument(
"-t",
"--gen_text",
type=str,
help="Text to generate.",
)
parser.add_argument(
"-f",
"--gen_file",
type=str,
help="File with text to generate. Ignores --gen_text",
)
parser.add_argument(
"-o",
"--output_dir",
type=str,
help="Path to output folder..",
)
parser.add_argument(
"-w",
"--output_file",
type=str,
help="Filename of output file..",
)
parser.add_argument(
"--remove_silence",
help="Remove silence.",
)
parser.add_argument("--vocoder_name", type=str, default="vocos", choices=["vocos", "bigvgan"], help="vocoder name")
parser.add_argument(
"--load_vocoder_from_local",
action="store_true",
help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz",
)
parser.add_argument(
"--speed",
type=float,
default=1.0,
help="Adjust the speed of the audio generation (default: 1.0)",
)
args = parser.parse_args()
config = tomli.load(open(args.config, "rb"))
ref_audio = args.ref_audio if args.ref_audio else config["ref_audio"]
ref_text = args.ref_text if args.ref_text != "666" else config["ref_text"]
gen_text = args.gen_text if args.gen_text else config["gen_text"]
gen_file = args.gen_file if args.gen_file else config["gen_file"]
# patches for pip pkg user
if "infer/examples/" in ref_audio:
ref_audio = str(files("f5_tts").joinpath(f"{ref_audio}"))
if "infer/examples/" in gen_file:
gen_file = str(files("f5_tts").joinpath(f"{gen_file}"))
if "voices" in config:
for voice in config["voices"]:
voice_ref_audio = config["voices"][voice]["ref_audio"]
if "infer/examples/" in voice_ref_audio:
config["voices"][voice]["ref_audio"] = str(files("f5_tts").joinpath(f"{voice_ref_audio}"))
if gen_file:
gen_text = codecs.open(gen_file, "r", "utf-8").read()
output_dir = args.output_dir if args.output_dir else config["output_dir"]
output_file = args.output_file if args.output_file else config["output_file"]
model = args.model if args.model else config["model"]
ckpt_file = args.ckpt_file if args.ckpt_file else ""
vocab_file = args.vocab_file if args.vocab_file else ""
remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
speed = args.speed
wave_path = Path(output_dir) / output_file
# spectrogram_path = Path(output_dir) / "infer_cli_out.png"
vocoder_name = args.vocoder_name
mel_spec_type = args.vocoder_name
if vocoder_name == "vocos":
vocoder_local_path = "../checkpoints/vocos-mel-24khz"
elif vocoder_name == "bigvgan":
vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
vocoder = load_vocoder(vocoder_name=mel_spec_type, is_local=args.load_vocoder_from_local, local_path=vocoder_local_path)
# load models
if model == "F5-TTS":
model_cls = DiT
model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
if ckpt_file == "":
if vocoder_name == "vocos":
repo_name = "F5-TTS"
exp_name = "F5TTS_Base"
ckpt_step = 1200000
ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
# ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors; local path
elif vocoder_name == "bigvgan":
repo_name = "F5-TTS"
exp_name = "F5TTS_Base_bigvgan"
ckpt_step = 1250000
ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.pt"))
elif model == "E2-TTS":
assert vocoder_name == "vocos", "E2-TTS only supports vocoder vocos"
model_cls = UNetT
model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
if ckpt_file == "":
repo_name = "E2-TTS"
exp_name = "E2TTS_Base"
ckpt_step = 1200000
ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
# ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors; local path
print(f"Using {model}...")
ema_model = load_model(model_cls, model_cfg, ckpt_file, mel_spec_type=mel_spec_type, vocab_file=vocab_file)
def main_process(ref_audio, ref_text, text_gen, model_obj, mel_spec_type, remove_silence, speed):
main_voice = {"ref_audio": ref_audio, "ref_text": ref_text}
if "voices" not in config:
voices = {"main": main_voice}
else:
voices = config["voices"]
voices["main"] = main_voice
for voice in voices:
voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
voices[voice]["ref_audio"], voices[voice]["ref_text"]
)
print("Voice:", voice)
print("Ref_audio:", voices[voice]["ref_audio"])
print("Ref_text:", voices[voice]["ref_text"])
generated_audio_segments = []
reg1 = r"(?=\[\w+\])"
chunks = re.split(reg1, text_gen)
reg2 = r"\[(\w+)\]"
for text in chunks:
if not text.strip():
continue
match = re.match(reg2, text)
if match:
voice = match[1]
else:
print("No voice tag found, using main.")
voice = "main"
if voice not in voices:
print(f"Voice {voice} not found, using main.")
voice = "main"
text = re.sub(reg2, "", text)
gen_text = text.strip()
ref_audio = voices[voice]["ref_audio"]
ref_text = voices[voice]["ref_text"]
print(f"Voice: {voice}")
audio, final_sample_rate, spectragram = infer_process(
ref_audio, ref_text, gen_text, model_obj, vocoder, mel_spec_type=mel_spec_type, speed=speed
)
generated_audio_segments.append(audio)
if generated_audio_segments:
final_wave = np.concatenate(generated_audio_segments)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(wave_path, "wb") as f:
sf.write(f.name, final_wave, final_sample_rate)
# Remove silence
if remove_silence:
remove_silence_for_generated_wav(f.name)
print(f.name)
def main():
main_process(ref_audio, ref_text, gen_text, ema_model, mel_spec_type, remove_silence, speed)
if __name__ == "__main__":
main()
|