Spaces:
Running
on
Zero
Running
on
Zero
Commit
·
025dfc0
1
Parent(s):
ae5ab93
add session info
Browse files
app.py
CHANGED
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@@ -3,11 +3,11 @@ import torch
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import gradio as gr
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import spaces
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import gc
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from pathlib import Path
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from pydub import AudioSegment
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import numpy as np
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import os
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import tempfile
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import gradio.themes as gr_themes
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import csv
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@@ -17,6 +17,24 @@ MODEL_NAME="nvidia/parakeet-tdt-0.6b-v2"
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model = ASRModel.from_pretrained(model_name=MODEL_NAME)
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model.eval()
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def get_audio_segment(audio_path, start_second, end_second):
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if not audio_path or not Path(audio_path).exists():
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print(f"Warning: Audio path '{audio_path}' not found or invalid for clipping.")
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@@ -55,7 +73,7 @@ def get_audio_segment(audio_path, start_second, end_second):
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return None
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@spaces.GPU
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def get_transcripts_and_raw_times(audio_path):
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if not audio_path:
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gr.Error("No audio file path provided for transcription.", duration=None)
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# Return an update to hide the button
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@@ -64,9 +82,9 @@ def get_transcripts_and_raw_times(audio_path):
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vis_data = [["N/A", "N/A", "Processing failed"]]
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raw_times_data = [[0.0, 0.0]]
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processed_audio_path = None
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temp_file = None
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csv_file_path = None
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original_path_name = Path(audio_path).name
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try:
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try:
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@@ -105,16 +123,14 @@ def get_transcripts_and_raw_times(audio_path):
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if resampled or mono:
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try:
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audio.export(
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temp_file.close()
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transcribe_path = processed_audio_path
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info_path_name = f"{original_path_name} (processed)"
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except Exception as export_e:
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gr.Error(f"Failed to export processed audio: {export_e}", duration=None)
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if
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os.remove(
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# Return an update to hide the button
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return [["Error", "Error", "Export failed"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
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else:
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@@ -139,12 +155,10 @@ def get_transcripts_and_raw_times(audio_path):
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# Default button update (hidden) in case CSV writing fails
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button_update = gr.DownloadButton(visible=False)
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try:
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writer = csv.writer(
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writer.writerow(csv_headers)
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writer.writerows(vis_data)
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csv_file_path = temp_csv_file.name
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temp_csv_file.close()
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print(f"CSV transcript saved to temporary file: {csv_file_path}")
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# If CSV is saved, create update to show button with path
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button_update = gr.DownloadButton(value=csv_file_path, visible=True)
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@@ -285,6 +299,9 @@ with gr.Blocks(theme=nvidia_theme) as demo:
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current_audio_path_state = gr.State(None)
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raw_timestamps_list_state = gr.State([])
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with gr.Tabs():
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with gr.TabItem("Audio File"):
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file_input = gr.Audio(sources=["upload"], type="filepath", label="Upload Audio File")
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@@ -313,14 +330,14 @@ with gr.Blocks(theme=nvidia_theme) as demo:
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mic_transcribe_btn.click(
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fn=get_transcripts_and_raw_times,
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inputs=[mic_input],
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outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
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api_name="transcribe_mic"
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)
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file_transcribe_btn.click(
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fn=get_transcripts_and_raw_times,
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inputs=[file_input],
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outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
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api_name="transcribe_file"
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)
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@@ -331,7 +348,9 @@ with gr.Blocks(theme=nvidia_theme) as demo:
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outputs=[selected_segment_player],
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)
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if __name__ == "__main__":
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print("Launching Gradio Demo...")
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demo.queue()
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demo.launch()
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import gradio as gr
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import spaces
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import gc
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import shutil
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from pathlib import Path
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from pydub import AudioSegment
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import numpy as np
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import os
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import gradio.themes as gr_themes
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import csv
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model = ASRModel.from_pretrained(model_name=MODEL_NAME)
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model.eval()
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def start_session(request: gr.Request):
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session_hash = request.session_hash
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session_dir = Path(f'/tmp/{session_hash}')
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session_dir.mkdir(parents=True, exist_ok=True)
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print(f"Session with hash {session_hash} started.")
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return session_dir.as_posix()
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def end_session(request: gr.Request):
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session_hash = request.session_hash
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session_dir = Path(f'/tmp/{session_hash}')
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if session_dir.exists():
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shutil.rmtree(session_dir)
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print(f"Session with hash {session_hash} ended.")
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def get_audio_segment(audio_path, start_second, end_second):
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if not audio_path or not Path(audio_path).exists():
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print(f"Warning: Audio path '{audio_path}' not found or invalid for clipping.")
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return None
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@spaces.GPU
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def get_transcripts_and_raw_times(audio_path, session_dir):
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if not audio_path:
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gr.Error("No audio file path provided for transcription.", duration=None)
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# Return an update to hide the button
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vis_data = [["N/A", "N/A", "Processing failed"]]
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raw_times_data = [[0.0, 0.0]]
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processed_audio_path = None
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csv_file_path = None
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original_path_name = Path(audio_path).name
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audio_name = Path(audio_path).stem
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try:
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try:
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if resampled or mono:
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try:
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processed_audio_path = Path(session_dir, f"{audio_name}_resampled.wav")
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audio.export(processed_audio_path, format="wav")
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transcribe_path = processed_audio_path.as_posix()
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info_path_name = f"{original_path_name} (processed)"
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except Exception as export_e:
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gr.Error(f"Failed to export processed audio: {export_e}", duration=None)
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if processed_audio_path and os.path.exists(processed_audio_path):
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os.remove(processed_audio_path)
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# Return an update to hide the button
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return [["Error", "Error", "Export failed"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
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else:
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# Default button update (hidden) in case CSV writing fails
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button_update = gr.DownloadButton(visible=False)
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try:
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csv_file_path = Path(session_dir, f"transcription_{audio_name}.csv")
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writer = csv.writer(open(csv_file_path, 'w'))
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writer.writerow(csv_headers)
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writer.writerows(vis_data)
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print(f"CSV transcript saved to temporary file: {csv_file_path}")
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# If CSV is saved, create update to show button with path
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button_update = gr.DownloadButton(value=csv_file_path, visible=True)
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current_audio_path_state = gr.State(None)
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raw_timestamps_list_state = gr.State([])
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session_dir = gr.State()
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demo.load(start_session, outputs=[session_dir])
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with gr.Tabs():
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with gr.TabItem("Audio File"):
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file_input = gr.Audio(sources=["upload"], type="filepath", label="Upload Audio File")
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mic_transcribe_btn.click(
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fn=get_transcripts_and_raw_times,
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inputs=[mic_input, session_dir],
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outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
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api_name="transcribe_mic"
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)
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file_transcribe_btn.click(
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fn=get_transcripts_and_raw_times,
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inputs=[file_input, session_dir],
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outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
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api_name="transcribe_file"
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)
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outputs=[selected_segment_player],
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)
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demo.unload(end_session)
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if __name__ == "__main__":
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print("Launching Gradio Demo...")
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demo.queue()
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demo.launch()
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