Update app.py
Browse files
app.py
CHANGED
@@ -3,9 +3,10 @@ import whisper
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import torch
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import os
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from pydub import AudioSegment, silence
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from faster_whisper import WhisperModel
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import numpy as np
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from scipy.io import wavfile
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# Mapping of model names to Whisper model sizes
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MODELS = {
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@@ -187,141 +188,74 @@ def remove_silence(audio_file, silence_threshold=-40, min_silence_len=500):
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return output_path
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def
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"""
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Args:
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Returns:
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str: Path to the
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audio = AudioSegment.from_file(audio_file)
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wav_path = "converted_audio.wav"
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audio.export(wav_path, format="wav")
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return wav_path
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def detect_voice_activity(audio_file, threshold=0.02):
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"""
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#
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for
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if
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for segment in
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# Export the trimmed audio
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output_path = "voice_trimmed_audio.wav"
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wavfile.write(output_path, sample_rate, trimmed_audio_int16)
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# Clean up the converted WAV file
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os.remove(wav_path)
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return output_path
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def detect_and_trim_audio(audio_file, threshold=0.02):
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"""
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Detect voice activity in the audio file, trim the audio to include only voice segments,
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and return the timestamps of the detected segments.
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Args:
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audio_file (str): Path to the input audio file.
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threshold (float): Amplitude threshold for voice detection. Default is 0.02.
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Returns:
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str: Path to the output audio file with only voice segments.
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list: List of timestamps (start, end) for the detected segments.
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"""
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# Convert the input audio to WAV format
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wav_path = convert_to_wav(audio_file)
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# Load the WAV file
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sample_rate, data = wavfile.read(wav_path)
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# If the audio is stereo, convert it to mono by averaging the channels
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if len(data.shape) > 1:
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data = np.mean(data, axis=1)
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# Normalize the audio data to the range [-1, 1]
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if data.dtype != np.float32:
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data = data.astype(np.float32) / np.iinfo(data.dtype).max
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# Detect voice activity
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voice_segments = []
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is_voice = False
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start = 0
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for i, sample in enumerate(data):
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if abs(sample) > threshold and not is_voice:
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is_voice = True
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start = i
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elif abs(sample) <= threshold and is_voice:
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is_voice = False
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voice_segments.append((start, i))
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# If the last segment is voice, add it
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if is_voice:
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voice_segments.append((start, len(data)))
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# Trim the audio to include only voice segments
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trimmed_audio = np.array([], dtype=np.float32)
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for segment in voice_segments:
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trimmed_audio = np.concatenate((trimmed_audio, data[segment[0]:segment[1]]))
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# Convert the trimmed audio back to 16-bit integer format
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trimmed_audio_int16 = np.int16(trimmed_audio * 32767)
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# Export the trimmed audio
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output_path = "
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# Calculate timestamps in seconds
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timestamps = [(start / sample_rate, end / sample_rate) for start, end in voice_segments]
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#
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return output_path,
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def transcribe_audio(audio_file, language="Auto Detect", model_size="Faster Whisper Large v3"):
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"""Transcribe the audio file."""
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@@ -373,7 +307,7 @@ def transcribe_audio(audio_file, language="Auto Detect", model_size="Faster Whis
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# Define the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("# Audio
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with gr.Tab("Detect Language"):
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gr.Markdown("Upload an audio file to detect its language.")
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silence_output = gr.Audio(label="Processed Audio (Silence Removed)", type="filepath")
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silence_button = gr.Button("Remove Silence")
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with gr.Tab("
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gr.Markdown("Upload
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)
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timestamps_output = gr.Textbox(label="Detected Timestamps (seconds)")
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# Link buttons to functions
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detect_button.click(detect_language, inputs=detect_audio_input, outputs=detect_language_output)
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inputs=[silence_audio_input, silence_threshold_slider, min_silence_len_slider],
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outputs=silence_output
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)
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detect_and_trim_audio,
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inputs=[
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outputs=[
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)
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# Launch the Gradio interface
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import torch
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import os
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from pydub import AudioSegment, silence
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from faster_whisper import WhisperModel
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import numpy as np
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from scipy.io import wavfile
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from scipy.signal import correlate
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# Mapping of model names to Whisper model sizes
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MODELS = {
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return output_path
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def detect_and_trim_audio(main_audio, target_audio, threshold=0.5):
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"""
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Detect the target audio in the main audio and trim the main audio to include only the detected segments.
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Args:
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main_audio (str): Path to the main audio file.
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target_audio (str): Path to the target audio file.
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threshold (float): Detection threshold (0 to 1). Higher values mean stricter detection.
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Returns:
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str: Path to the trimmed audio file.
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str: Detected timestamps in the format "start-end (in seconds)".
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"""
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# Load audio files
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main_rate, main_data = wavfile.read(main_audio)
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target_rate, target_data = wavfile.read(target_audio)
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# Ensure both audio files have the same sample rate
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if main_rate != target_rate:
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raise ValueError("Sample rates of the main audio and target audio must match.")
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# Normalize audio data
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main_data = main_data.astype(np.float32) / np.iinfo(main_data.dtype).max
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target_data = target_data.astype(np.float32) / np.iinfo(target_data.dtype).max
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# Perform cross-correlation to detect the target audio in the main audio
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correlation = correlate(main_data, target_data, mode='valid')
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correlation = np.abs(correlation)
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max_corr = np.max(correlation)
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# Detect segments where the target audio is present
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detected_segments = []
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for i, corr_value in enumerate(correlation):
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if corr_value >= threshold * max_corr:
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start_time = i / main_rate
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end_time = (i + len(target_data)) / main_rate
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detected_segments.append((start_time, end_time))
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# Merge overlapping or nearby segments
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merged_segments = []
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for segment in detected_segments:
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if not merged_segments:
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merged_segments.append(segment)
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else:
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last_segment = merged_segments[-1]
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if segment[0] <= last_segment[1] + 1.0: # Merge if within 1 second
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merged_segments[-1] = (last_segment[0], max(last_segment[1], segment[1]))
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else:
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merged_segments.append(segment)
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# Trim the main audio to include only the detected segments
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main_audio_segment = AudioSegment.from_file(main_audio)
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trimmed_audio = AudioSegment.empty()
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timestamps = []
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for segment in merged_segments:
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start_ms = int(segment[0] * 1000)
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end_ms = int(segment[1] * 1000)
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trimmed_audio += main_audio_segment[start_ms:end_ms]
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timestamps.append(f"{segment[0]:.2f}-{segment[1]:.2f}")
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# Export the trimmed audio
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output_path = "trimmed_audio.wav"
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trimmed_audio.export(output_path, format="wav")
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# Format timestamps
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timestamps_str = "\n".join(timestamps)
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return output_path, timestamps_str
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def transcribe_audio(audio_file, language="Auto Detect", model_size="Faster Whisper Large v3"):
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"""Transcribe the audio file."""
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# Define the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("# Audio Processing Tool")
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with gr.Tab("Detect Language"):
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gr.Markdown("Upload an audio file to detect its language.")
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silence_output = gr.Audio(label="Processed Audio (Silence Removed)", type="filepath")
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silence_button = gr.Button("Remove Silence")
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with gr.Tab("Detect and Trim Audio"):
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gr.Markdown("Upload a main audio file and a target audio file. The app will detect the target audio in the main audio and trim it.")
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main_audio_input = gr.Audio(type="filepath", label="Upload Main Audio File")
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target_audio_input = gr.Audio(type="filepath", label="Upload Target Audio File")
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threshold_slider = gr.Slider(
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minimum=0.1, maximum=1.0, value=0.5, step=0.1,
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label="Detection Threshold",
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info="Higher values mean stricter detection."
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)
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trimmed_audio_output = gr.Audio(label="Trimmed Audio", type="filepath")
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timestamps_output = gr.Textbox(label="Detected Timestamps (in seconds)")
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detect_button = gr.Button("Detect and Trim")
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# Link buttons to functions
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detect_button.click(detect_language, inputs=detect_audio_input, outputs=detect_language_output)
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inputs=[silence_audio_input, silence_threshold_slider, min_silence_len_slider],
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outputs=silence_output
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)
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detect_button.click(
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detect_and_trim_audio,
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inputs=[main_audio_input, target_audio_input, threshold_slider],
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outputs=[trimmed_audio_output, timestamps_output]
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)
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# Launch the Gradio interface
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