forked from camenduru/ai-voice-cloning
UI cleanup, actually fix syncing the epoch counter (i hope), setting auto-suggest voice chunk size whatever to 0 will just split based on the average duration length, signal when a NaN info value is detected (there's some safeties in the training, but it will inevitably fuck the model)
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287738a338
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5be14abc21
23
src/utils.py
23
src/utils.py
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@ -233,7 +233,7 @@ def generate(
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if emotion == "Custom":
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if prompt and prompt.strip() != "":
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cut_text = f"[{prompt},] {cut_text}"
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else:
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elif emotion != "None":
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cut_text = f"[I am really {emotion.lower()},] {cut_text}"
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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@ -464,14 +464,21 @@ def update_baseline_for_latents_chunks( voice ):
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return 1
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files = os.listdir(path)
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total = 0
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total_duration = 0
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for file in files:
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if file[-4:] != ".wav":
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continue
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metadata = torchaudio.info(f'{path}/{file}')
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duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
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total_duration += duration
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total = total + 1
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if args.autocalculate_voice_chunk_duration_size == 0:
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return int(total_duration / total) if total > 0 else 1
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return int(total_duration / args.autocalculate_voice_chunk_duration_size) if total_duration > 0 else 1
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def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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@ -550,6 +557,8 @@ class TrainingState():
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self.eta = "?"
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self.eta_hhmmss = "?"
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self.nan_detected = False
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self.last_info_check_at = 0
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self.statistics = []
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self.losses = []
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@ -701,13 +710,10 @@ class TrainingState():
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info_line = line.split("INFO:")[-1]
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# to-do, actually validate this works, and probably kill training when it's found, the model's dead by this point
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if ': nan' in info_line:
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should_return = True
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print("! NAN DETECTED !")
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self.buffer.append("! NAN DETECTED !")
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self.nan_detected = True
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# easily rip out our stats...
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match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', info_line)
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match = re.findall(r'\b([a-z_0-9]+?)\b: *?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', info_line)
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if match and len(match) > 0:
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for k, v in match:
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self.info[k] = float(v.replace(",", ""))
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@ -862,6 +868,8 @@ class TrainingState():
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self.metrics['loss'] = ", ".join(self.metrics['loss'])
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message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
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if self.nan_detected:
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message = f"[!NaN DETECTED!] {message}"
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if message:
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percent = self.it / float(self.its) # self.epoch / float(self.epochs)
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@ -965,7 +973,6 @@ def stop_training():
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try:
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children = [p.info for p in psutil.process_iter(attrs=['pid', 'name', 'cmdline']) if './src/train.py' in p.info['cmdline']]
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except Exception as e:
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print(e)
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pass
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training_state.process.stdout.close()
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@ -1419,7 +1426,7 @@ def setup_args():
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'prune-nonfinal-outputs': True,
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'use-bigvgan-vocoder': True,
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'concurrency-count': 2,
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'autocalculate-voice-chunk-duration-size': 10,
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'autocalculate-voice-chunk-duration-size': 0,
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'output-sample-rate': 44100,
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'output-volume': 1,
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47
src/webui.py
47
src/webui.py
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@ -180,9 +180,9 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
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return (
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gr.update(value=j, visible=j is not None),
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gr.update(visible=j is not None),
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gr.update(value=latents, visible=latents is not None),
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None if j is None else j['voice']
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None if j is None else j['voice'],
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gr.update(visible=j is not None),
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)
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def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ):
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@ -378,15 +378,15 @@ def setup_gradio():
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with gr.Tab("Generate"):
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with gr.Row():
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with gr.Column():
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text = gr.Textbox(lines=4, label="Prompt")
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text = gr.Textbox(lines=4, label="Input Prompt")
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with gr.Row():
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with gr.Column():
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delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"], value="Custom", label="Emotion", type="value", interactive=True )
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
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prompt = gr.Textbox(lines=1, label="Custom Emotion")
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voice = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
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mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath" )
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mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
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voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=128, value=1, step=1)
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with gr.Row():
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refresh_voices = gr.Button(value="Refresh Voice List")
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@ -397,6 +397,11 @@ def setup_gradio():
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inputs=voice,
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outputs=voice_latents_chunks
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)
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voice.change(
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fn=lambda value: gr.update(visible=value == "microphone"),
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inputs=voice,
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outputs=mic_audio,
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)
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with gr.Column():
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candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
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seed = gr.Number(value=0, precision=0, label="Seed")
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@ -406,16 +411,17 @@ def setup_gradio():
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
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diffusion_sampler = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="P", label="Diffusion Samplers", type="value" )
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show_experimental_settings = gr.Checkbox(label="Show Experimental Settings")
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reset_generation_settings_button = gr.Button(value="Reset to Default")
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with gr.Column(visible=False) as col:
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experimental_column = col
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experimental_checkboxes = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
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diffusion_sampler = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="DDIM", label="Diffusion Samplers", type="value"
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)
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cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
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top_p = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
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diffusion_temperature = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
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@ -460,10 +466,12 @@ def setup_gradio():
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audio_in = gr.Files(type="file", label="Audio Input", file_types=["audio"])
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import_voice_name = gr.Textbox(label="Voice Name")
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import_voice_button = gr.Button(value="Import Voice")
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with gr.Column():
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metadata_out = gr.JSON(label="Audio Metadata", visible=False)
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copy_button = gr.Button(value="Copy Settings", visible=False)
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latents_out = gr.File(type="binary", label="Voice Latents", visible=False)
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with gr.Column(visible=False) as col:
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utilities_metadata_column = col
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metadata_out = gr.JSON(label="Audio Metadata")
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copy_button = gr.Button(value="Copy Settings")
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latents_out = gr.File(type="binary", label="Voice Latents")
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with gr.Tab("Training"):
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with gr.Tab("Prepare Dataset"):
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with gr.Row():
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@ -662,9 +670,9 @@ def setup_gradio():
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inputs=audio_in,
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outputs=[
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metadata_out,
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copy_button,
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latents_out,
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import_voice_name
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import_voice_name,
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utilities_metadata_column,
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]
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)
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@ -697,9 +705,10 @@ def setup_gradio():
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outputs=voice,
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)
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prompt.change(fn=lambda value: gr.update(value="Custom"),
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inputs=prompt,
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outputs=emotion
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emotion.change(
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fn=lambda value: gr.update(visible=value == "Custom"),
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inputs=emotion,
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outputs=prompt
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)
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mic_audio.change(fn=lambda value: gr.update(value="microphone"),
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inputs=mic_audio,
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