Update app.py

app.py

@@ -7,38 +7,6 @@ import os
 import re
 from pathlib import Path
 from pydub import AudioSegment
-import librosa
-import soundfile as sf
-import numpy as np
-from pydub import AudioSegment
-from pydub.playback import play
-from scipy.signal import butter, lfilter # Ensure this line is present
-
-
-def apply_low_pass_filter(audio_segment, cutoff_freq, sample_rate, order=5):
-    """Applies a low-pass filter to a pydub AudioSegment."""
-    audio_np = np.array(audio_segment.get_array_of_samples()).astype(np.float32) / (2**15 - 1)
-    if audio_segment.channels == 2:
-        audio_np = audio_np.reshape(-1, 2)
-
-    nyquist_freq = 0.5 * sample_rate
-    normalized_cutoff = cutoff_freq / nyquist_freq
-    b, a = butter(order, normalized_cutoff, btype='low', analog=False)
-
-    filtered_data = np.zeros_like(audio_np, dtype=np.float32)
-    if audio_segment.channels == 1:
-        filtered_data = lfilter(b, a, audio_np)
-    else:
-        for channel in range(audio_segment.channels):
-            filtered_data[:, channel] = lfilter(b, a, audio_np[:, channel])
-
-    filtered_data_int16 = (filtered_data * (2**15 - 1)).astype(np.int16)
-    filtered_audio = AudioSegment(filtered_data_int16.tobytes(),
-                                 frame_rate=sample_rate,
-                                 sample_width=audio_segment.sample_width,
-                                 channels=audio_segment.channels)
-    return filtered_audio
-
 
 def get_silence(duration_ms=1000):
     # Create silent audio segment with specified parameters
@@ -46,9 +14,11 @@ def get_silence(duration_ms=1000):
         duration=duration_ms,
         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(
@@ -66,212 +36,193 @@ def get_silence(duration_ms=1000):
 
 # Get all available voices
 async def get_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 {}
+    voices = await edge_tts.list_voices()
+    return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}
 
-async def generate_audio_with_voice_prefix(text_segment, default_voice, rate, pitch, overall_target_duration_ms=None, speed_adjustment_factor=1.0):
-    """Generates audio for a text segment, handling voice prefixes and adjusting rate for duration."""
+async def generate_audio_with_voice_prefix(text_segment, default_voice, rate, pitch):
+    """Generates audio for a text segment, handling voice prefixes."""
     current_voice_full = default_voice
     current_voice_short = current_voice_full.split(" - ")[0] if current_voice_full else ""
     current_rate = rate
     current_pitch = pitch
     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
-    #iterate throught the voice map to see if a match if found, if found then set the voice
-    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 = 0
-            elif prefix in ["1O", "4V"]:
-                current_pitch = -20
-                current_rate = -10
-            detect = 1
-            processed_text = processed_text[len(prefix):].strip()  #this removes the Prefix and leave only number or text after it.
-            break
-    #match = re.search(r'([A-Za-z]+)-?(\d+)', processed_text)
-    match = re.search(r"^(-?\d+)\s*(.*)", processed_text)    
+    voice1_full = "en-AU-WilliamNeural - en-AU (Male)"
+    voice1_short = voice1_full.split(" - ")[0]
+    voice1F_full ="en-GB-SoniaNeural - en-GB (Female)"
+    voice1F_short = voice1F_full.split(" - ")[0]
+    voice2_full = "en-GB-RyanNeural - en-GB (Male)"
+    voice2_short = voice2_full.split(" - ")[0]
+    voice2F_full = "en-US-JennyNeural - en-US (Female)"
+    voice2F_short = voice2F_full.split(" - ")[0]
+    voice3_full ="en-US-BrianMultilingualNeural - en-US (Male)"  #good for reading
+    voice3_short = voice3_full.split(" - ")[0]
+    voice3F_full = "en-HK-YanNeural - en-HK (Female)"
+    voice3F_short = voice3F_full.split(" - ")[0]
+    voice4_full = "en-GB-ThomasNeural - en-GB (Male)"
+    voice4_short = voice4_full.split(" - ")[0]
+    voice4F_full ="en-US-EmmaNeural - en-US (Female)"
+    voice4F_short = voice4F_full.split(" - ")[0]
+    voice5_full = "en-GB-RyanNeural - en-GB (Male)" #Old Man
+    voice5_short = voice5_full.split(" - ")[0]
+    voice6_full = "en-GB-MaisieNeural - en-GB (Female)"  #Child
+    voice6_short = voice6_full.split(" - ")[0]
+    voice7_full = "vi-VN-HoaiMyNeural - vi-VN (Female)"  #Vietnamese
+    voice7_short = voice7_full.split(" - ")[0]
+    voice8_full = "vi-VN-NamMinhNeural - vi-VN (Male)"  #Vietnamese
+    voice8_short = voice8_full.split(" - ")[0]
+    voice9F_full = "de-DE-SeraphinaMultilingualNeural - de-DE (Female)"  #Vietnamese
+    voice9F_short = voice7_full.split(" - ")[0]
+    voice9_full = "ko-KR-HyunsuMultilingualNeural - ko-KR (Male)"  #Vietnamese
+    voice9_short = voice8_full.split(" - ")[0]
+    detect=0
+    if processed_text.startswith("1F"):
+        current_voice_short = voice1F_short
+        current_pitch = 25
+        detect=1
+        #processed_text = processed_text[2:].strip()
+    elif processed_text.startswith("2F"):
+        current_voice_short = voice2F_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("3F"):
+        current_voice_short = voice3F_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("4F"):
+        current_voice_short = voice4F_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("1M"):
+        current_voice_short = voice1_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("2M"):
+        current_voice_short = voice2_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("3M"):
+        current_voice_short = voice3_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("4M"):
+        current_voice_short = voice4_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("1O"):  # Old man voice
+        current_voice_short = voice5_short
+        current_pitch = -20
+        current_rate = -10
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("1C"):  #Child voice
+        current_voice_short = voice6_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("1V"):  #Female VN
+        current_voice_short = voice7_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("2V"):
+        current_voice_short = voice8_short
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("3V"):  #Female VN
+        current_voice_short = voice9F_short
+        current_pitch = 25
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    elif processed_text.startswith("4V"):
+        current_voice_short = voice9_short
+        current_pitch = -20
+        #processed_text = processed_text[2:].strip()
+        detect=1
+    #Looking for number following prefix, which are pitch values.
+    #match = re.search(r'[A-Za-z]\d+', part)  # Look for a letter followed by one or more digits
+    match = re.search(r'[A-Za-z]+\-?\d+', processed_text)  # Look for a letter(s) followed by an optional '-' and digits
     if match:
-        #prefix_pitch = match.group(1)
-        number = match.group(1)
-        print(f"Prefix match found.") # Debug
-        current_pitch += int(number)
-        #processed_text = re.sub(r'[A-Za-z]+-?\d+', '', processed_text, count=1).strip()
-        #processed_text = re.sub(r'([A-Za-z]+)([-]?\d*)', '', processed_text, count=1).strip()
-        processed_text = match.group(2)
-    #elif detect:
-    #       processed_text = processed_text.lstrip('-0123456789').strip() # Remove potential leftover numbers
-
+        # Extract the prefix (e.g., '2F') and number (e.g., '-20')
+        prefix = ''.join([ch for ch in match.group() if ch.isalpha()])  # Extract letters (prefix)
+        number = int(''.join([ch for ch in match.group() if ch.isdigit() or ch == '-']))  # Extract digits (number)
+        current_pitch += number
+        # Step 2: Remove the found number from the string
+        new_text = re.sub(r'[A-Za-z]+\-?\d+', '', processed_text, count=1).strip()  # Remove prefix and number (e.g., '2F-20')
+        #processed_text = new_text[2:]  #cut out the prefix like 1F, 3M etc
+        processed_text = new_text[len(prefix):]  # Dynamically remove the prefix part
+    else:
+        if detect:
+            processed_text = processed_text[2:]
     if processed_text:
         rate_str = f"{current_rate:+d}%"
         pitch_str = f"{current_pitch:+d}Hz"
-        print(f"Sending to Edge: '{processed_text}'") # Debug
-        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 os.path.exists(audio_path):
-                audio = AudioSegment.from_mp3(audio_path)
-                # Trim leading and trailing silence
-                def detect_leading_silence(sound, silence_threshold=-50.0, chunk_size=10):
-                    trim_ms = 0
-                    assert chunk_size > 0 # to avoid infinite loop
-                    while sound[trim_ms:trim_ms+chunk_size].dBFS < silence_threshold and trim_ms < len(sound):
-                        trim_ms += chunk_size
-                    return trim_ms
-
-                start_trim = detect_leading_silence(audio)
-                end_trim = detect_leading_silence(audio.reverse())
-                trimmed_audio = audio[start_trim:len(audio)-end_trim]
-                trimmed_audio.export(audio_path, format="mp3") # Overwrite with trimmed version
-                return audio_path
-
-        except Exception as e:
-            print(f"Edge TTS error processing '{processed_text}': {e}")
-            return None
+        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)
+        return audio_path
     return None
 
-async def process_transcript_line(line, next_line_start_time, default_voice, rate, pitch, overall_duration_ms, speed_adjustment_factor):
-    """Processes a single transcript line with HH:MM:SS,milliseconds timestamp."""
-    match = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+(.*)', line)
+async def process_transcript_line(line, default_voice, rate, pitch):
+    """Processes a single transcript line with HH:MM:SS.milliseconds timestamp and quoted text segments."""
+    match = re.match(r'(\d{2}):(\d{2}):(\d{2})\.(\d{3})\s+(.*)', line)
     if match:
-        start_h, start_m, start_s, start_ms, text_parts = match.groups()
+        hours, minutes, seconds, milliseconds, text_parts = match.groups()
         start_time_ms = (
-            int(start_h) * 3600000 +
-            int(start_m) * 60000 +
-            int(start_s) * 1000 +
-            int(start_ms)
+            int(hours) * 3600000 +
+            int(minutes) * 60000 +
+            int(seconds) * 1000 +
+            int(milliseconds)
         )
         audio_segments = []
-        split_parts = re.split(r'[“”"]', text_parts)
+        split_parts = re.split(r'(")', text_parts)  # Split by quote marks, keeping the quotes
+
         process_next = False
         for part in split_parts:
             if part == '"':
                 process_next = not process_next
                 continue
             if process_next and part.strip():
-                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch, overall_duration_ms, speed_adjustment_factor)
+                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch)
                 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, overall_duration_ms, speed_adjustment_factor)
+                audio_path = await generate_audio_with_voice_prefix(part, default_voice, rate, pitch) # Process unquoted text with default voice
                 if audio_path:
                     audio_segments.append(audio_path)
 
-        if audio_segments:
-            combined_audio = AudioSegment.empty()
-            for segment_path in audio_segments:
-                try:
-                    segment = AudioSegment.from_mp3(segment_path)
-                    combined_audio += segment
-                    os.remove(segment_path) # Clean up individual segment files
-                except Exception as e:
-                    print(f"Error loading or combining audio segment {segment_path}: {e}")
-                    return None, None, None
-
-            combined_audio_path = f"combined_audio_{start_time_ms}.mp3"
-            try:
-                combined_audio.export(combined_audio_path, format="mp3")
-                return start_time_ms, [combined_audio_path], overall_duration_ms
-            except Exception as e:
-                print(f"Error exporting combined audio: {e}")
-                return None, None, None
-
-        return start_time_ms, [], overall_duration_ms # Return empty list if no audio generated
+        return start_time_ms, audio_segments
+    return None, None
 
-    return None, None, None
-
-async def transcript_to_speech(transcript_text, voice, rate, pitch, speed_adjustment_factor):
+async def transcript_to_speech(transcript_text, voice, rate, pitch):
     if not transcript_text.strip():
         return None, gr.Warning("Please enter transcript text.")
     if not voice:
         return None, gr.Warning("Please select a voice.")
+
     lines = transcript_text.strip().split('\n')
     timed_audio_segments = []
     max_end_time_ms = 0
 
-    for i, line in enumerate(lines):
-        next_line_start_time = None
-        if i < len(lines) - 1:
-            next_line_match = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+.*', lines[i+1])
-            if next_line_match:
-                nh, nm, ns, nms = next_line_match.groups()
-                next_line_start_time = (
-                    int(nh) * 3600000 +
-                    int(nm) * 60000 +
-                    int(ns) * 1000 +
-                    int(nms)
-                )
-
-        current_line_match = re.match(r'(\d{2}):(\d{2}):(\d{2}),(\d{3})\s+(.*)', line)
-        if current_line_match:
-            sh, sm, ss, sms, text_content = current_line_match.groups()
-            start_time_ms = (
-                int(sh) * 3600000 +
-                int(sm) * 60000 +
-                int(ss) * 1000 +
-                int(sms)
-            )
-            overall_duration_ms = None
-            if next_line_start_time is not None:
-                overall_duration_ms = next_line_start_time - start_time_ms
-
-            start_time, audio_paths, duration = await process_transcript_line(line, next_line_start_time, voice, rate, pitch, overall_duration_ms, speed_adjustment_factor)
-
-            if start_time is not None and audio_paths:
-                combined_line_audio = AudioSegment.empty()
-                total_generated_duration_ms = 0
-                for path in audio_paths:
-                    if path:
-                        try:
-                            audio = AudioSegment.from_mp3(path)
-                            combined_line_audio += audio
-                            total_generated_duration_ms += len(audio)
-                            os.remove(path)
-                        except FileNotFoundError:
-                            print(f"Warning: Audio file not found: {path}")
-
-                if combined_line_audio and overall_duration_ms is not None and overall_duration_ms > 0 and total_generated_duration_ms > overall_duration_ms:
-                    speed_factor = (total_generated_duration_ms / overall_duration_ms) * speed_adjustment_factor
-                    if speed_factor > 0:
-                        if speed_factor < 1.0:
-                            speed_factor = 1.0
-                        combined_line_audio = combined_line_audio.speedup(playback_speed=speed_factor)
-
-                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
+    for line in lines:
+        start_time, audio_paths = await process_transcript_line(line, voice, rate, pitch)
+        if start_time is not None and audio_paths:
+            combined_line_audio = AudioSegment.empty()
+            for path in audio_paths:
+                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:
+                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."
@@ -279,35 +230,26 @@ async def transcript_to_speech(transcript_text, voice, rate, pitch, speed_adjust
     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'])
-        
-    # Apply the low-pass filter here
-    cutoff_frequency = 3500  # 3.5 kHz (you can make this a user-configurable parameter later)
-    filtered_final_audio = apply_low_pass_filter(final_audio, cutoff_frequency, final_audio.frame_rate)
 
     combined_audio_path = tempfile.mktemp(suffix=".mp3")
-    # Export the *filtered* audio here
-    filtered_final_audio.export(combined_audio_path, format="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):
-    audio, warning = asyncio.run(transcript_to_speech(transcript, voice, rate, pitch, speed_adjustment_factor))
+def tts_interface(transcript, voice, rate, pitch):
+    audio, warning = asyncio.run(transcript_to_speech(transcript, voice, rate, pitch))
     return audio, warning
 
 async def create_demo():
     voices = await get_voices()
     default_voice = "en-US-AndrewMultilingualNeural - en-US (Male)"
     description = """
-    Process timestamped text (HH:MM:SS,milliseconds) with voice changes within quotes.
-    The duration for each line is determined by the timestamp of the following line.
-    The speed of the ENTIRE generated audio for a line will be adjusted to fit within this duration.
-    If there is no subsequent timestamp, the speed adjustment will be skipped.
-    You can control the intensity of the speed adjustment using the "Speed Adjustment Factor" slider.
-    Format: `HH:MM:SS,milliseconds "VoicePrefix Text" more text "AnotherVoicePrefix More Text"`
+    Process timestamped text (HH:MM:SS.milliseconds) with voice changes within quotes.
+    Format: `HH:MM:SS.milliseconds "VoicePrefix Text" more text "AnotherVoicePrefix More Text"`
     Example:
     ```
-    00:00:00,000 "This is the default voice." more default. "1F Now a female voice." and back to default.
-    00:00:05,500 "1C Yes," said the child, "it is fun!"
+    00:00:00.000 "This is the default voice." more default. "1F Now a female voice." and back to default.
+    00:00:05.000 "1C Yes," said the child, "it is fun!"
     ```
     ***************************************************************************************************
     1M = en-AU-WilliamNeural - en-AU (Male)
@@ -329,17 +271,16 @@ async def create_demo():
     demo = gr.Interface(
         fn=tts_interface,
         inputs=[
-            gr.Textbox(label="Timestamped Text with Voice Changes and Duration", lines=10, placeholder='00:00:00,000 "Text" more text "1F Different Voice"'),
+            gr.Textbox(label="Timestamped Text with Voice Changes", lines=10, placeholder='00:00:00.000 "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")
+            gr.Slider(minimum=-50, maximum=50, value=0, label="Pitch Adjustment (Hz)", step=1)
         ],
         outputs=[
             gr.Audio(label="Generated Audio", type="filepath"),
             gr.Markdown(label="Warning", visible=False)
         ],
-        title="TTS with Line-Wide Duration Adjustment and In-Quote Voice Switching",
+        title="TTS with HH:MM:SS.milliseconds and In-Quote Voice Switching",
         description=description,
         analytics_enabled=False,
         allow_flagging=False
@@ -348,4 +289,4 @@ async def create_demo():
 
 if __name__ == "__main__":
     demo = asyncio.run(create_demo())
-    demo.launch()
+    demo.launch()