forked from mrq/ai-voice-cloning
auto-suggested voice chunk size is based on the total duration of the voice files divided by 10 seconds, added setting to adjust the auto-suggested division factor (a really oddly worded one), because I'm sure people will OOM blindly generating without adjusting this slider
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26
src/utils.py
26
src/utils.py
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@ -454,6 +454,22 @@ def hash_file(path, algo="md5", buffer_size=0):
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return "{0}".format(hash.hexdigest())
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def update_baseline_for_latents_chunks( voice ):
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path = f'{get_voice_dir()}/{voice}/'
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if not os.path.isdir(path):
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return 1
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files = os.listdir(path)
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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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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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global tts
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global args
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@ -1244,6 +1260,7 @@ 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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'output-sample-rate': 44100,
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'output-volume': 1,
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@ -1282,6 +1299,7 @@ def setup_args():
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parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
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parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
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parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
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parser.add_argument("--autocalculate-voice-chunk-duration-size", type=float, default=default_arguments['autocalculate-voice-chunk-duration-size'], help="Number of seconds to suggest voice chunk size for (for example, 100 seconds of audio at 10 seconds per chunk will suggest 10 chunks)")
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parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
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parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
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@ -1321,7 +1339,7 @@ def setup_args():
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return args
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, use_bigvgan_vocoder, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, use_bigvgan_vocoder, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
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global args
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args.listen = listen
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@ -1340,7 +1358,8 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
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args.voice_fixer = voice_fixer
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args.voice_fixer_use_cuda = voice_fixer_use_cuda
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args.concurrency_count = concurrency_count
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args.output_sample_rate = output_sample_rate
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args.output_sample_rate = 44000
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args.autocalculate_voice_chunk_duration_size = autocalculate_voice_chunk_duration_size
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args.output_volume = output_volume
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args.autoregressive_model = autoregressive_model
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@ -1372,6 +1391,7 @@ def save_args_settings():
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'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
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'concurrency-count': args.concurrency_count,
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'output-sample-rate': args.output_sample_rate,
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'autocalculate-voice-chunk-duration-size': args.autocalculate_voice_chunk_duration_size,
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'output-volume': args.output_volume,
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'autoregressive-model': args.autoregressive_model,
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@ -1481,7 +1501,7 @@ def load_tts( restart=False, model=None ):
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
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except Exception as e:
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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update_autoregressive_model(args.autoregressive_model)
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load_autoregressive_model(args.autoregressive_model)
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if not hasattr(tts, 'autoregressive_model_hash'):
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tts.autoregressive_model_hash = hash_file(tts.autoregressive_model_path)
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28
src/webui.py
28
src/webui.py
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@ -385,19 +385,6 @@ def setup_gradio():
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refresh_voices = gr.Button(value="Refresh Voice List")
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recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
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def update_baseline_for_latents_chunks( voice ):
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path = f'{get_voice_dir()}/{voice}/'
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if not os.path.isdir(path):
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return 1
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files = os.listdir(path)
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count = 0
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for file in files:
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if file[-4:] == ".wav":
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count += 1
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return count if count > 0 else 1
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voice.change(
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fn=update_baseline_for_latents_chunks,
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inputs=voice,
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@ -575,7 +562,7 @@ def setup_gradio():
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exec_inputs = exec_inputs + [
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gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
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gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count),
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gr.Number(label="Output Sample Rate", precision=0, value=args.output_sample_rate),
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gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size),
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gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
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]
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@ -594,6 +581,7 @@ def setup_gradio():
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inputs=autoregressive_model_dropdown,
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outputs=None
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)
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# kill_button = gr.Button(value="Close UI")
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def update_model_list_proxy( val ):
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autoregressive_models = get_autoregressive_models()
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@ -814,6 +802,18 @@ def setup_gradio():
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outputs=save_yaml_output #console_output
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)
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"""
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def kill_process():
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ui.close()
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exit()
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kill_button.click(
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kill_process,
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inputs=None,
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outputs=None
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)
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"""
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if os.path.isfile('./config/generate.json'):
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ui.load(import_generate_settings, inputs=None, outputs=input_settings)
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