Upload AgentF5TTSChunk.py

AgentF5TTSChunk.py

@@ -1,190 +1,145 @@
-import os
-import re
-import time
-import logging
-import subprocess
-from f5_tts.api import F5TTS 
-
-
-
-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,
-    )
-
-
-
-
+import os
+import re
+import time
+import logging
+import json
+import subprocess
+import gradio as gr
+from f5_tts.api import F5TTS
+
+# Constants
+CONFIG_FILE = "last_inputs.json"
+
+# Initialize logging
+logging.basicConfig(level=logging.INFO)
+
+class AgentF5TTS:
+    def __init__(self, ckpt_file, vocoder_name="vocos", delay=0, device="cuda"):
+        self.model = F5TTS(ckpt_file=ckpt_file, vocoder_name=vocoder_name, device=device)
+        self.delay = delay
+    
+    def generate_emotion_speech(self, text, output_audio_folder, speaker_emotion_refs, convert_to_mp3=False):
+        lines = [line.strip() for line in text.split("\n") if line.strip()]
+
+        if not lines:
+            logging.error("Input text is empty.")
+            return
+
+        if not output_audio_folder:
+            logging.error("Output audio folder is not specified.")
+            return None
+        if not os.path.exists(output_audio_folder):
+            os.makedirs(output_audio_folder, exist_ok=True)
+        output_audio_file = os.path.join(output_audio_folder, "generated_audio.wav")
+        temp_files = []
+
+        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
+
+            temp_file = os.path.join(output_audio_folder, f"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="",  # Placeholder or load corresponding 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)
+        return output_audio_file
+    
+    def _determine_speaker_emotion(self, text):
+        speaker, emotion = "speaker1", "neutral"
+        match = re.search(r"\[speaker:(.*?), emotion:(.*?)\]", text)
+        if match:
+            speaker = match.group(1).strip()
+            emotion = match.group(2).strip()
+        return speaker, emotion
+
+    def combine_audio_files(self, temp_files, output_audio_file, convert_to_mp3):
+        if not temp_files:
+            logging.error("No audio files to combine.")
+            return
+
+        list_file = os.path.join(os.path.dirname(output_audio_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}")
+
+# Load last inputs from JSON file
+def load_last_inputs():
+    if os.path.exists(CONFIG_FILE):
+        with open(CONFIG_FILE, "r") as f:
+            return json.load(f)
+    return {}
+
+# Save inputs to JSON file
+def save_last_inputs(inputs):
+    with open(CONFIG_FILE, "w") as f:
+        json.dump(inputs, f, indent=4)
+
+# Gradio Interface
+def gradio_interface(model_path, vocoder_name, delay, device, text, output_audio_folder, ref_audio, convert_to_mp3, speaker1_happy, speaker1_sad, speaker1_angry, speaker1_neutral):
+    if not os.path.exists(model_path.name):
+        logging.error(f"Model path does not exist: {model_path.name}")
+        return None
+    agent = AgentF5TTS(ckpt_file=model_path.name, vocoder_name=vocoder_name, delay=delay, device=device)
+    speaker_emotion_refs = {
+        ("speaker1", "happy"): speaker1_happy.name,
+        ("speaker1", "sad"): speaker1_sad.name,
+        ("speaker1", "angry"): speaker1_angry.name,
+        ("speaker1", "neutral"): speaker1_neutral.name,
+    }
+    if not os.path.exists(output_audio_folder):
+        logging.error(f"Output audio folder does not exist: {output_audio_folder}")
+        return None
+    output_file = agent.generate_emotion_speech(text, output_audio_folder, speaker_emotion_refs, convert_to_mp3)
+    return output_file if os.path.exists(output_file) else None
+
+# Launch Gradio App
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.File(label="Model Path"),
+        gr.Dropdown(label="Vocoder", choices=["vocos", "bigvgan"]),
+        gr.Number(label="Delay (seconds)"),
+        gr.Dropdown(label="Device", choices=["cpu", "cuda"]),
+        gr.Textbox(label="Input Text"),
+        gr.Textbox(label="Output Audio Folder"),
+        gr.File(label="Reference Audio File"),
+        gr.Checkbox(label="Convert to MP3"),
+        gr.File(label="Speaker1 Happy Reference Audio"),
+        gr.File(label="Speaker1 Sad Reference Audio"),
+        gr.File(label="Speaker1 Angry Reference Audio"),
+        gr.File(label="Speaker1 Neutral Reference Audio")
+    ],
+    outputs=gr.Audio(label="Generated Audio"),
+    title="F5-TTS Text-to-Speech Generator",
+    description="Generate speech from text using the F5-TTS model."
+)
+
+iface.launch()