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cf7fa42 | 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 | import os
import re
import time
import logging
import subprocess
from typing import Optional, Dict, List, Tuple, Union
from f5_tts.api import F5TTS
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class AgentF5TTS:
def __init__(self, ckpt_file: str, vocoder_name: str = "vocos", delay: float = 0, device: str = "mps"):
"""
Initialize the F5-TTS Agent.
:param ckpt_file: Path to the safetensors model checkpoint.
:param vocoder_name: Name of the vocoder to use ("vocos" or "bigvgan"). (Ignored in current F5TTS version)
:param delay: Delay in seconds between audio generations.
:param device: Device to use ("cpu", "cuda", "mps").
"""
# vocoder_name is not supported in the installed version of F5TTS.__init__
self.model = F5TTS(ckpt_file=ckpt_file, device=device)
self.delay = delay # Delay in seconds
def infer(self, ref_file: str, ref_text: str, gen_text: str, file_wave: str, remove_silence: bool = False, speed: float = 1.0):
"""
Direct inference method wrapping the underlying model.
:param ref_file: Path to reference audio file.
:param ref_text: Reference text (optional).
:param gen_text: Text to generate.
:param file_wave: Output wave file path.
:param remove_silence: Whether to remove silence from generated audio.
:param speed: Speed factor for speech generation.
"""
self.model.infer(
ref_file=ref_file,
ref_text=ref_text,
gen_text=gen_text,
file_wave=file_wave,
remove_silence=remove_silence,
speed=speed,
)
def generate_emotion_speech(self, text_file: str, output_audio_file: str, speaker_emotion_refs: Dict[Tuple[str, str], str], convert_to_mp3: bool = False):
"""
Generate speech using the F5-TTS model.
:param text_file: Path to the input text file.
:param output_audio_file: Path to save the combined audio output.
:param speaker_emotion_refs: Dictionary mapping (speaker, emotion) tuples to reference audio paths.
:param convert_to_mp3: Boolean flag to convert the output to MP3.
"""
try:
with open(text_file, "r", encoding="utf-8") as file:
lines = [line.strip() for line in file if line.strip()]
except FileNotFoundError:
logging.error(f"Text file not found: {text_file}")
return
if not lines:
logging.error("Input text file is empty.")
return
temp_files = []
os.makedirs(os.path.dirname(output_audio_file), exist_ok=True)
for i, line in enumerate(lines):
speaker, emotion = self._determine_speaker_emotion(line)
ref_audio = speaker_emotion_refs.get((speaker, emotion))
line_clean = re.sub(r'\[speaker:.*?\]\s*', '', line)
if not ref_audio or not os.path.exists(ref_audio):
logging.error(f"Reference audio not found for speaker '{speaker}', emotion '{emotion}'.")
continue
ref_text = "" # Placeholder or load corresponding text
temp_file = f"{output_audio_file}_line{i + 1}.wav"
try:
logging.info(f"Generating speech for line {i + 1}: '{line_clean}' with speaker '{speaker}', emotion '{emotion}'")
self.model.infer(
ref_file=ref_audio,
ref_text=ref_text,
gen_text=line_clean,
file_wave=temp_file,
remove_silence=True,
)
temp_files.append(temp_file)
time.sleep(self.delay)
except Exception as e:
logging.error(f"Error generating speech for line {i + 1}: {e}")
self._combine_audio_files(temp_files, output_audio_file, convert_to_mp3)
def generate_speech(self, text_file: str, output_audio_file: str, ref_audio: str, convert_to_mp3: bool = False):
try:
with open(text_file, 'r', encoding='utf-8') as file:
lines = [line.strip() for line in file if line.strip()]
except FileNotFoundError:
logging.error(f"Text file not found: {text_file}")
return
if not lines:
logging.error("Input text file is empty.")
return
temp_files = []
os.makedirs(os.path.dirname(output_audio_file), exist_ok=True)
for i, line in enumerate(lines):
if not ref_audio or not os.path.exists(ref_audio):
logging.error(f"Reference audio not found for speaker.")
continue
temp_file = f"{output_audio_file}_line{i + 1}.wav"
try:
logging.info(f"Generating speech for line {i + 1}: '{line}'")
self.model.infer(
ref_file=ref_audio, # No reference audio
ref_text="", # No reference text
gen_text=line,
file_wave=temp_file,
)
temp_files.append(temp_file)
except Exception as e:
logging.error(f"Error generating speech for line {i + 1}: {e}")
# Combine temp_files into output_audio_file if needed
self._combine_audio_files(temp_files, output_audio_file, convert_to_mp3)
def _determine_speaker_emotion(self, text: str) -> Tuple[str, str]:
"""
Extract speaker and emotion from the text using regex.
Default to "speaker1" and "neutral" if not specified.
"""
speaker, emotion = "speaker1", "neutral" # Default values
# Use regex to find [speaker:speaker_name, emotion:emotion_name]
match = re.search(r"\[speaker:(.*?), emotion:(.*?)\]", text)
if match:
speaker = match.group(1).strip()
emotion = match.group(2).strip()
logging.debug(f"Determined speaker: '{speaker}', emotion: '{emotion}'")
return speaker, emotion
def _combine_audio_files(self, temp_files: List[str], output_audio_file: str, convert_to_mp3: bool):
"""Combine multiple audio files into a single file using FFmpeg."""
if not temp_files:
logging.error("No audio files to combine.")
return
list_file = "file_list.txt"
with open(list_file, "w") as f:
for temp in temp_files:
f.write(f"file '{temp}'\n")
try:
subprocess.run(["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", list_file, "-c", "copy", output_audio_file], check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
logging.info(f"Combined audio saved to: {output_audio_file}")
if convert_to_mp3:
mp3_output = output_audio_file.replace(".wav", ".mp3")
subprocess.run(["ffmpeg", "-y", "-i", output_audio_file, "-codec:a", "libmp3lame", "-qscale:a", "2", mp3_output], check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
logging.info(f"Converted to MP3: {mp3_output}")
for temp in temp_files:
os.remove(temp)
os.remove(list_file)
except Exception as e:
logging.error(f"Error combining audio files: {e}")
# Example usage, remove from this line on to import into other agents.
# make sure to adjust the paths to yourr files.
if __name__ == "__main__":
env = os.environ.copy()
env["PYTHONUNBUFFERED"] = "1"
model_path = "./F5-TTS/ckpts/pt-br/model_last.safetensors"
speaker_emotion_refs = {
("speaker1", "happy"): "ref_audios/speaker1_happy.wav",
("speaker1", "sad"): "ref_audios/speaker1_sad.wav",
("speaker1", "angry"): "ref_audios/speaker1_angry.wav",
}
# Note: Adjust path if needed
if os.path.exists(model_path):
agent = AgentF5TTS(ckpt_file=model_path, vocoder_name="vocos", delay=6)
# Test generate_emotion_speech
# agent.generate_emotion_speech(...)
# Test generate_speech
# agent.generate_speech(...)
else:
print(f"Model path {model_path} does not exist. Skipping example execution.")
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