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Update app.py
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app.py
CHANGED
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@@ -23,21 +23,17 @@ def format_time(seconds):
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# Function to split text into segments by punctuation or limit to 7-8 words
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def split_text_into_segments(text):
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segments = []
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# Split by punctuation (., !, ?)
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raw_segments = re.split(r'([.!?])', text)
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for i in range(0, len(raw_segments) - 1, 2):
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# Combine segment with following punctuation
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sentence = raw_segments[i].strip() + raw_segments[i + 1]
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words = sentence.split()
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# If segment is longer than 8 words, split into 7-8 word chunks
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if len(words) > 8:
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for j in range(0, len(words), 8):
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segments.append(" ".join(words[j:j + 8]))
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else:
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segments.append(sentence.strip())
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# Handle remaining text after the last punctuation
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if len(raw_segments) % 2 == 1:
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remaining_text = raw_segments[-1].strip()
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words = remaining_text.split()
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@@ -46,11 +42,11 @@ def split_text_into_segments(text):
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return segments
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# Function to generate SRT with accurate timing per batch
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async def generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate, voice):
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audio_file = f"batch_{batch_num}_audio.wav"
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# Generate the audio using edge-tts
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tts = edge_tts.Communicate(batch_text, voice, rate=rate, pitch=f"{pitch}Hz")
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await tts.save(audio_file)
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@@ -67,7 +63,6 @@ async def generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate
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for index, segment in enumerate(segments):
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end_time = start_time + segment_duration
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# If end_time exceeds actual audio length of the batch, adjust it
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if end_time > start_offset + actual_length:
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end_time = start_offset + actual_length
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@@ -75,35 +70,28 @@ async def generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate
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srt_content += f"{format_time(start_time)} --> {format_time(end_time)}\n"
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srt_content += segment + "\n\n"
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# Update start time for next segment
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start_time = end_time
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return srt_content, audio_file, start_time
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# Batch processing function
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async def batch_process_srt_and_audio(script_text, pitch, rate, voice, progress=gr.Progress()):
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batches = [script_text[i:i + 500] for i in range(0, len(script_text), 500)]
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all_srt_content = ""
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combined_audio = AudioSegment.empty()
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start_offset = 0.0
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# Process each batch sequentially to ensure proper timing and cumulative offset tracking
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for batch_num, batch_text in enumerate(batches):
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srt_content, audio_file, end_offset = await generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate, voice)
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all_srt_content += srt_content
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# Append the audio of each batch to the combined audio
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batch_audio = AudioSegment.from_file(audio_file)
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combined_audio += batch_audio
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start_offset = end_offset
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# Clean up the individual batch audio file
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os.remove(audio_file)
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# Update progress
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progress((batch_num + 1) / len(batches))
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# Final cross-check: Adjust any subtitle that exceeds the total audio length
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total_audio_length = combined_audio.duration_seconds
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validated_srt_content = ""
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for line in all_srt_content.strip().splitlines():
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@@ -116,15 +104,12 @@ async def batch_process_srt_and_audio(script_text, pitch, rate, voice, progress=
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line = f"{format_time(start_time)} --> {format_time(end_time)}"
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validated_srt_content += line + "\n"
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# Generate unique names for the final files
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unique_id = uuid.uuid4()
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final_audio_path = f"final_audio_{unique_id}.mp3"
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final_srt_path = f"final_subtitles_{unique_id}.srt"
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# Export combined audio directly as MP3 with 320 kbps bitrate
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combined_audio.export(final_audio_path, format="mp3", bitrate="320k")
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# Export validated SRT with unique names
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with open(final_srt_path, "w") as srt_file:
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srt_file.write(validated_srt_content)
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@@ -132,12 +117,11 @@ async def batch_process_srt_and_audio(script_text, pitch, rate, voice, progress=
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# Gradio interface function
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async def process_script(script_text, pitch, rate, voice):
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srt_path, audio_path = await batch_process_srt_and_audio(script_text, pitch, formatted_rate, voice)
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return srt_path, audio_path, audio_path
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# Gradio interface setup
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voice_options = {
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"Andrew": "en-US-AndrewNeural",
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"Jenny": "en-US-JennyNeural",
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@@ -183,16 +167,17 @@ app = gr.Interface(
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fn=process_script,
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inputs=[
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gr.Textbox(label="Enter Script Text", lines=10),
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gr.Slider(label="Pitch Adjustment (Hz)", minimum=-100, maximum=100, step=1, value=1),
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gr.Slider(label="Rate Adjustment (%)", minimum=-100, maximum=100, step=1, value=1),
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gr.Dropdown(label="Select Voice", choices=list(voice_options.keys()), value="Andrew")
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],
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outputs=[
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gr.File(label="Download SRT"),
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gr.File(label="Download Audio"),
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gr.Audio(label="Audio Playback")
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],
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live=
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)
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app.launch()
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# Function to split text into segments by punctuation or limit to 7-8 words
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def split_text_into_segments(text):
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segments = []
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raw_segments = re.split(r'([.!?])', text)
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for i in range(0, len(raw_segments) - 1, 2):
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sentence = raw_segments[i].strip() + raw_segments[i + 1]
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words = sentence.split()
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if len(words) > 8:
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for j in range(0, len(words), 8):
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segments.append(" ".join(words[j:j + 8]))
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else:
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segments.append(sentence.strip())
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if len(raw_segments) % 2 == 1:
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remaining_text = raw_segments[-1].strip()
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words = remaining_text.split()
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return segments
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# Function to generate SRT with accurate timing per batch
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async def generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate, voice):
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audio_file = f"batch_{batch_num}_audio.wav"
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# Generate the audio using edge-tts
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tts = edge_tts.Communicate(batch_text, voice, rate=rate, pitch=f"{pitch}Hz")
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await tts.save(audio_file)
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for index, segment in enumerate(segments):
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end_time = start_time + segment_duration
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if end_time > start_offset + actual_length:
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end_time = start_offset + actual_length
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srt_content += f"{format_time(start_time)} --> {format_time(end_time)}\n"
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srt_content += segment + "\n\n"
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start_time = end_time
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return srt_content, audio_file, start_time
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# Batch processing function
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async def batch_process_srt_and_audio(script_text, pitch, rate, voice, progress=gr.Progress()):
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batches = [script_text[i:i + 500] for i in range(0, len(script_text), 500)]
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all_srt_content = ""
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combined_audio = AudioSegment.empty()
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start_offset = 0.0
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for batch_num, batch_text in enumerate(batches):
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srt_content, audio_file, end_offset = await generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate, voice)
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all_srt_content += srt_content
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batch_audio = AudioSegment.from_file(audio_file)
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combined_audio += batch_audio
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start_offset = end_offset
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os.remove(audio_file)
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progress((batch_num + 1) / len(batches))
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total_audio_length = combined_audio.duration_seconds
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validated_srt_content = ""
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for line in all_srt_content.strip().splitlines():
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line = f"{format_time(start_time)} --> {format_time(end_time)}"
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validated_srt_content += line + "\n"
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unique_id = uuid.uuid4()
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final_audio_path = f"final_audio_{unique_id}.mp3"
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final_srt_path = f"final_subtitles_{unique_id}.srt"
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combined_audio.export(final_audio_path, format="mp3", bitrate="320k")
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with open(final_srt_path, "w") as srt_file:
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srt_file.write(validated_srt_content)
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# Gradio interface function
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async def process_script(script_text, pitch, rate, voice):
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formatted_rate = f"{'+' if rate > 0 else ''}{int(rate)}%"
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srt_path, audio_path = await batch_process_srt_and_audio(script_text, pitch, formatted_rate, voice_options[voice])
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return srt_path, audio_path, audio_path
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# Gradio interface setup
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voice_options = {
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"Andrew": "en-US-AndrewNeural",
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"Jenny": "en-US-JennyNeural",
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fn=process_script,
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inputs=[
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gr.Textbox(label="Enter Script Text", lines=10),
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gr.Slider(label="Pitch Adjustment (Hz)", minimum=-100, maximum=100, step=1, value=1),
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gr.Slider(label="Rate Adjustment (%)", minimum=-100, maximum=100, step=1, value=1),
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gr.Dropdown(label="Select Voice", choices=list(voice_options.keys()), value="Andrew"),
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gr.Button(label="Submit") # Add submit button
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],
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outputs=[
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gr.File(label="Download SRT"),
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gr.File(label="Download Audio"),
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gr.Audio(label="Audio Playback")
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],
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live=False # Changed to False to enable the Submit button functionality
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)
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app.launch()
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