Adjusts added agent and organize, readme adjusts.

AgentF5TTSChunk.py

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+import os
+import re
+import time
+import logging
+import subprocess
+from f5_tts.api import F5TTS  # Correct import for F5-TTS API
+
+
+
+logging.basicConfig(level=logging.INFO)
+
+
+class AgentF5TTS:
+    def __init__(self, ckpt_file, vocoder_name="vocos", delay=0, device="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").
+        :param delay: Delay in seconds between audio generations.
+        :param device: Device to use ("cpu", "cuda", "mps").
+        """
+        self.model = F5TTS(ckpt_file=ckpt_file, vocoder_name=vocoder_name, device=device)
+        self.delay = delay  # Delay in seconds
+
+    def generate_emotion_speech(self, text_file, output_audio_file, speaker_emotion_refs, convert_to_mp3=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 = 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}' with speaker '{speaker}', emotion '{emotion}'")
+                self.model.infer(
+                    ref_file=ref_audio,
+                    ref_text=ref_text,
+                    gen_text=line,
+                    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, output_audio_file, ref_audio, convert_to_mp3=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):
+        """
+        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.info(f"Determined speaker: '{speaker}', emotion: '{emotion}'")
+        return speaker, emotion
+
+    def _combine_audio_files(self, temp_files, output_audio_file, convert_to_mp3):
+        """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)
+            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)
+                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",
+    }
+    agent = AgentF5TTS(ckpt_file=model_path, vocoder_name="vocos", delay=6)
+    
+    agent.generate_emotion_speech(
+        text_file="input_text.txt",
+        output_audio_file="output/final_output_emo.wav",
+        speaker_emotion_refs=speaker_emotion_refs,
+        convert_to_mp3=True,
+    )
+    
+    agent.generate_speech(
+        text_file="input_text2.txt",
+        output_audio_file="output/final_output.wav",
+        ref_audio="ref_audios/refaudio.mp3",
+        convert_to_mp3=True,
+    )
+
+
+
+