Revert to previous code

app.py

@@ -1,133 +1,150 @@
-import spaces
-import gradio as gr
-import edge_tts
-import asyncio
-import tempfile
-import os
-import re
-from pathlib import Path
-from pydub import AudioSegment
-import librosa
 import soundfile as sf
 import numpy as np
 
-# Global constant for voice mapping
-VOICE_MAP = {
-    "1F": "en-GB-SoniaNeural",
-    "2M": "en-GB-RyanNeural",
-    "3M": "en-US-BrianMultilingualNeural",
-    "2F": "en-US-JennyNeural",
-    "1M": "en-AU-WilliamNeural",
-    "3F": "en-HK-YanNeural",
-    "4M": "en-GB-ThomasNeural",
-    "4F": "en-US-EmmaNeural",
-    "1O": "en-GB-RyanNeural",  # Old Man
-    "1C": "en-GB-MaisieNeural",  # Child
-    "1V": "vi-VN-HoaiMyNeural",  # Vietnamese (Female)
-    "2V": "vi-VN-NamMinhNeural",  # Vietnamese (Male)
-    "3V": "vi-VN-HoaiMyNeural",  # Vietnamese (Female)
-    "4V": "vi-VN-NamMinhNeural",  # Vietnamese (Male)
-}
-
 def get_silence(duration_ms=1000):
-    """Creates a silent AudioSegment."""
-    return AudioSegment.silent(
+    # Create silent audio segment with specified parameters
+    silent_audio = AudioSegment.silent(
         duration=duration_ms,
-        frame_rate=24000,
-        sample_width=4,
-        channels=1
+        frame_rate=24000  # 24kHz sampling rate
     )
-
+    # Set audio parameters
+    silent_audio = silent_audio.set_channels(1)  # Mono
+    silent_audio = silent_audio.set_sample_width(4)  # 32-bit (4 bytes per sample)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        # Export with specific bitrate and codec parameters
+        silent_audio.export(
+            tmp_file.name,
+            format="mp3",
+            bitrate="48k",
+            parameters=[
+                "-ac", "1",  # Mono
+                "-ar", "24000",  # Sample rate
+                "-sample_fmt", "s32",  # 32-bit samples
+                "-codec:a", "libmp3lame"  # MP3 codec
+            ]
+        )
+        return tmp_file.name
+
+# Get all available voices
 async def get_voices():
-    """Lists available Edge TTS voices."""
+
     try:
         voices = await edge_tts.list_voices()
         return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}
-    except Exception as e:
-        print(f"Error listing voices: {e}")
-        return {}
 
 async def generate_audio_with_voice_prefix(text_segment, default_voice, rate, pitch, target_duration_ms=None, speed_adjustment_factor=1.0):
     """Generates audio for a text segment, handling voice prefixes and adjusting rate for duration."""
-    processed_text = text_segment.strip()
-    current_voice_short = default_voice.split(" - ")[0] if default_voice else ""
+    current_voice_full = default_voice
+    current_voice_short = current_voice_full.split(" - ")[0] if current_voice_full else ""
     current_rate = rate
     current_pitch = pitch
-
-    for prefix, voice_short in VOICE_MAP.items():
+    processed_text = text_segment.strip()
+    print(f"Processing this  text segment: {processed_text}") # Debug
+    voice_map = {
+        "1F": "en-GB-SoniaNeural",
+        "2M": "en-GB-RyanNeural",
+        "3M": "en-US-BrianMultilingualNeural",
+        "2F": "en-US-JennyNeural",
+        "1M": "en-AU-WilliamNeural",
+        "3F": "en-HK-YanNeural",
+        "4M": "en-GB-ThomasNeural",
+        "4F": "en-US-EmmaNeural",
+        "1O": "en-GB-RyanNeural",  # Old Man
+        "1C": "en-GB-MaisieNeural",  # Child
+        "1V": "vi-VN-HoaiMyNeural",  # Vietnamese (Female)
+        "2V": "vi-VN-NamMinhNeural",  # Vietnamese (Male)
+        "3V": "vi-VN-HoaiMyNeural",  # Vietnamese (Female)
+        "4V": "vi-VN-NamMinhNeural",  # Vietnamese (Male)
+    }
+    detect = 0
+    for prefix, voice_short in voice_map.items():
         if processed_text.startswith(prefix):
             current_voice_short = voice_short
             if prefix in ["1F", "3F", "1V", "3V"]:
-                current_pitch = 25
             elif prefix in ["1O", "4V"]:
                 current_pitch = -20
                 current_rate = -10
+            detect = 1
             processed_text = processed_text[len(prefix):].strip()
             break
 
     match = re.search(r'([A-Za-z]+)-?(\d+)', processed_text)
-    if match and match.group(1) in VOICE_MAP:
-        pitch_adjustment = int(match.group(2))
-        current_pitch += pitch_adjustment
-        processed_text = re.sub(r'[A-Za-z]+-?\d+', '', processed_text, count=1).strip()
-    elif any(processed_text.startswith(prefix) for prefix in VOICE_MAP): # Handle leftover prefixes
-        processed_text = re.sub(r'^[A-Za-z]{1,2}', '', processed_text).lstrip('-').strip()
-
+    if match:
+        prefix_pitch = match.group(1)
+        number = int(match.group(2))
+        if prefix_pitch in voice_map:
+            current_pitch += number
+            processed_text = re.sub(r'[A-Za-z]+-?\d+', '', processed_text, count=1).strip()
+        elif detect:
+            processed_text = processed_text.lstrip('-0123456789').strip() # Remove potential leftover numbers
+    elif detect:
+        processed_text = processed_text[2:].strip()
     if processed_text:
         rate_str = f"{current_rate:+d}%"
         pitch_str = f"{current_pitch:+d}Hz"
-        try:
-            communicate = edge_tts.Communicate(processed_text, current_voice_short, rate=rate_str, pitch=pitch_str)
             with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
                 audio_path = tmp_file.name
                 await communicate.save(audio_path)
-
-                if target_duration_ms is not None and os.path.exists(audio_path) and target_duration_ms > 0:
-                    audio = AudioSegment.from_mp3(audio_path)
-                    audio_duration_ms = len(audio)
-                    if audio_duration_ms > target_duration_ms:
-                        speed_factor = (audio_duration_ms / target_duration_ms) * speed_adjustment_factor
-                        if speed_factor > 0 and speed_factor >= 1.0:
-                            y, sr = librosa.load(audio_path, sr=None)
-                            y_stretched = librosa.effects.time_stretch(y, rate=speed_factor)
-                            sf.write(audio_path, y_stretched, sr)
-                return audio_path
+            if target_duration_ms is not None and os.path.exists(audio_path):
+                audio = AudioSegment.from_mp3(audio_path)
+                audio_duration_ms = len(audio)
+                #print(f"Generated audio duration: {audio_duration_ms}ms, Target duration: {target_duration_ms}ms") # Debug
+                if audio_duration_ms > target_duration_ms and target_duration_ms > 0:
+                    speed_factor = (audio_duration_ms / target_duration_ms) * speed_adjustment_factor
+                    #print(f"Speed factor (after user adjustment): {speed_factor}") # Debug
+                    if speed_factor > 0:
+                        if speed_factor < 1.0:
+                            speed_factor = 1.0
+                        y, sr = librosa.load(audio_path, sr=None)
+                        y_stretched = librosa.effects.time_stretch(y, rate=speed_factor)
+                        sf.write(audio_path, y_stretched, sr)
+                else:
+                    print("Generated audio is not longer than target duration, no speed adjustment.") # Debug
+            return audio_path
         except Exception as e:
             print(f"Edge TTS error processing '{processed_text}': {e}")
             return None
     return None
 
 async def process_transcript_line(line, default_voice, rate, pitch, speed_adjustment_factor):
-    """Processes a single transcript line with timestamp and potential voice changes."""
-    match = re.match(r'(\d{2}:\d{2}:\d{2},\d{3})\s+-\s+(\d{2}:\d{2}:\d{2},\d{3})\s+(.*)', line)
+    """Processes a single transcript line with HH:MM:SS,milliseconds - HH:MM:SS,milliseconds timestamp."""
+    match = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+-\s+(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+(.*)', line)
     if match:
-        start_time_str, end_time_str, text_parts = match.groups()
-
-        def time_str_to_ms(time_str):
-            h, m, s_ms = time_str.split(':')
-            s, ms = s_ms.split(',')
-            return int(h) * 3600000 + int(m) * 60000 + int(s) * 1000 + int(ms)
-
-        start_time_ms = time_str_to_ms(start_time_str)
-        end_time_ms = time_str_to_ms(end_time_str)
+        start_h, start_m, start_s, start_ms, end_h, end_m, end_s, end_ms, text_parts = match.groups()
+        start_time_ms = (
+            int(start_h) * 3600000 +
+            int(start_m) * 60000 +
+            int(start_s) * 1000 +
+            int(start_ms)
+        )
+        end_time_ms = (
+            int(end_h) * 3600000 +
+            int(end_m) * 60000 +
+            int(end_s) * 1000 +
+            int(end_ms)
+        )
         duration_ms = end_time_ms - start_time_ms
 
         audio_segments = []
-        parts = re.split(r'([“”"])', text_parts)
-        in_quote = False
-        for part in parts:
+        split_parts = re.split(r'[“”"]', text_parts)
+        process_next = False
+        for part in split_parts:
             if part == '"':
-                in_quote = not in_quote
+                process_next = not process_next
                 continue
-            if part.strip():
-                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch, duration_ms, speed_adjustment_factor if in_quote else 1.0)
+            if process_next and part.strip():
+                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch, duration_ms, speed_adjustment_factor)
+                if audio_path:
+                    audio_segments.append(audio_path)
+            elif not process_next and part.strip():
+                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch, duration_ms, speed_adjustment_factor)
                 if audio_path:
                     audio_segments.append(audio_path)
         return start_time_ms, audio_segments, duration_ms
     return None, None, None
 
 async def transcript_to_speech(transcript_text, voice, rate, pitch, speed_adjustment_factor):
-    """Converts a timestamped transcript with voice changes to a single audio file."""
+
     if not transcript_text.strip():
         return None, gr.Warning("Please enter transcript text.")
     if not voice:
@@ -136,103 +153,47 @@ async def transcript_to_speech(transcript_text, voice, rate, pitch, speed_adjust
     lines = transcript_text.strip().split('\n')
     timed_audio_segments = []
     max_end_time_ms = 0
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        for line in lines:
-            start_time, audio_paths, duration = await process_transcript_line(line, voice, rate, pitch, speed_adjustment_factor)
-            if start_time is not None and audio_paths:
-                combined_line_audio = AudioSegment.empty()
-                for path in audio_paths:
-                    if path and os.path.exists(path):
-                        try:
-                            audio = AudioSegment.from_mp3(path)
-                            combined_line_audio += audio
-                        except FileNotFoundError:
-                            print(f"Warning: Audio file not found: {path}")
-                        finally:
-                            try:
-                                os.remove(path)
-                            except OSError:
-                                print(f"Warning: Could not remove temporary file: {path}")
-                if combined_line_audio:
-                    timed_audio_segments.append({'start': start_time, 'audio': combined_line_audio})
-                    max_end_time_ms = max(max_end_time_ms, start_time + len(combined_line_audio))
-            elif audio_paths:
-                for path in audio_paths:
-                    if path:
-                        try:
-                            os.remove(path)
-                        except FileNotFoundError:
-                            pass # Clean up even if no timestamp
-
-        if not timed_audio_segments:
-            return None, "No processable audio segments found."
-
-        final_audio = AudioSegment.silent(duration=max_end_time_ms, frame_rate=24000)
-        for segment in timed_audio_segments:
-            final_audio = final_audio.overlay(segment['audio'], position=segment['start'])
-
-        combined_audio_path = Path(tmpdir) / "combined_audio.mp3"
-        final_audio.export(str(combined_audio_path), format="mp3")
-        return str(combined_audio_path), None
+    for line in lines:
+        start_time, audio_paths, duration = await process_transcript_line(line, voice, rate, pitch, speed_adjustment_factor)
+        if start_time is not None and audio_paths:
+            combined_line_audio = AudioSegment.empty()
+            current_time_ms = start_time
+            segment_duration = duration / len(audio_paths) if audio_paths else 0
+            for path in audio_paths:
+                if path:  # Only process if audio_path is not None (meaning TTS was successful)
+                    try:
+                        audio = AudioSegment.from_mp3(path)
+                        combined_line_audio += audio
+                        os.remove(path)
+                    except FileNotFoundError:
+                        print(f"Warning: Audio file not found: {path}")
+            if combined_line_audio:
+                timed_audio_segments.append({'start': start_time, 'audio': combined_line_audio})
+                max_end_time_ms = max(max_end_time_ms, start_time + len(combined_line_audio))
+        elif audio_paths:
+            for path in audio_paths:
+                if path:
+                    try:
+                        os.remove(path)
+                    except FileNotFoundError:
+                        pass # Clean up even if no timestamp
+    if not timed_audio_segments:
+        return None, "No processable audio segments found."
+    final_audio = AudioSegment.silent(duration=max_end_time_ms, frame_rate=24000)
+    for segment in timed_audio_segments:
+        final_audio = final_audio.overlay(segment['audio'], position=segment['start'])
+    combined_audio_path = tempfile.mktemp(suffix=".mp3")
+    final_audio.export(combined_audio_path, format="mp3")
+    return combined_audio_path, None
 
 @spaces.GPU
 def tts_interface(transcript, voice, rate, pitch, speed_adjustment_factor):
-    """Gradio interface function for TTS."""
+
     audio, warning = asyncio.run(transcript_to_speech(transcript, voice, rate, pitch, speed_adjustment_factor))
     return audio, warning
 
 async def create_demo():
-    """Creates the Gradio demo interface."""
+
     voices = await get_voices()
     default_voice = "en-US-AndrewMultilingualNeural - en-US (Male)"
-    description = """
-    Process timestamped text (HH:MM:SS,milliseconds - HH:MM:SS,milliseconds) with voice changes within quotes.
-    The duration specified in the timestamp will be used to adjust the speech rate so the generated audio fits within that time.
-    You can control the intensity of the speed adjustment using the "Speed Adjustment Factor" slider.
-    Format: `HH:MM:SS,milliseconds - HH:MM:SS,milliseconds "VoicePrefix Text" more text "AnotherVoicePrefix More Text"`
-    Example:
-    ```
-    00:00:00,000 - 00:00:05,000 "This is the default voice." more default. "1F Now a female voice." and back to default.
-    00:00:05,500 - 00:00:10,250 "1C Yes," said the child, "it is fun!"
-    ```
-    ***************************************************************************************************
-    1M = en-AU-WilliamNeural - en-AU (Male)
-    1F = en-GB-SoniaNeural - en-GB (Female)
-    2M = en-GB-RyanNeural - en-GB (Male)
-    2F = en-US-JennyNeural - en-US (Female)
-    3M = en-US-BrianMultilingualNeural - en-US (Male)
-    3F = en-HK-YanNeural - en-HK (Female)
-    4M = en-GB-ThomasNeural - en-GB (Male)
-    4F = en-US-EmmaNeural - en-US (Female)
-    1O = en-GB-RyanNeural - en-GB (Male) # Old Man
-    1C = en-GB-MaisieNeural - en-GB (Female) # Child
-    1V = vi-VN-HoaiMyNeural - vi-VN (Female) # Vietnamese (Female)
-    2V = vi-VN-NamMinhNeural - vi-VN (Male) # Vietnamese (Male)
-    3V = vi-VN-HoaiMyNeural - vi-VN (Female) # Vietnamese (Female)
-    4V = vi-VN-NamMinhNeural - vi-VN (Male) # Vietnamese (Male)
-    ****************************************************************************************************
-    """
-    demo = gr.Interface(
-        fn=tts_interface,
-        inputs=[
-            gr.Textbox(label="Timestamped Text with Voice Changes and Duration", lines=10, placeholder='00:00:00,000 - 00:00:05,000 "Text" more text "1F Different Voice"'),
-            gr.Dropdown(choices=[""] + list(voices.keys()), label="Select Default Voice", value=default_voice),
-            gr.Slider(minimum=-50, maximum=50, value=0, label="Speech Rate Adjustment (%)", step=1),
-            gr.Slider(minimum=-50, maximum=50, value=0, label="Pitch Adjustment (Hz)", step=1),
-            gr.Slider(minimum=0.5, maximum=1.5, value=1.0, step=0.05, label="Speed Adjustment Factor")
-        ],
-        outputs=[
-            gr.Audio(label="Generated Audio", type="filepath"),
-            gr.Markdown(label="Warning", visible=False)
-        ],
-        title="TTS with Duration-Aware Speed Adjustment and In-Quote Voice Switching",
-        description=description,
-        analytics_enabled=False,
-        allow_flagging=False
-    )
-    return demo
-
-if __name__ == "__main__":
-    demo = asyncio.run(create_demo())
-    demo.launch()
+    description = """