forked from mrq/tortoise-tts
Added integration for "voicefixer", fixed issue where candidates>1 and lines>1 only outputs the last combined candidate, numbered step for each generation in progress, output time per generation step
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@ -217,6 +217,7 @@ Below are settings that override the default launch arguments. Some of these req
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* `Low VRAM`: disables optimizations in TorToiSe that increases VRAM consumption. Suggested if your GPU has under 6GiB.
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* `Embed Output Metadata`: enables embedding the settings and latents used to generate that audio clip inside that audio clip. Metadata is stored as a JSON string in the `lyrics` tag.
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* `Slimmer Computed Latents`: falls back to the original, 12.9KiB way of storing latents (without the extra bits required for using the CVVP model).
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* `Voice Fixer`: runs each generated audio clip through `voicefixer`, if available and installed.
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* `Voice Latent Max Chunk Size`: during the voice latents calculation pass, this limits how large, in bytes, a chunk can be. Large values can run into VRAM OOM errors.
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* `Sample Batch Size`: sets the batch size when generating autoregressive samples. Bigger batches result in faster compute, at the cost of increased VRAM consumption. Leave to 0 to calculate a "best" fit.
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* `Concurrency Count`: how many Gradio events the queue can process at once. Leave this over 1 if you want to modify settings in the UI that updates other settings while generating audio clips.
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@ -15,4 +15,5 @@ numpy
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numba
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gradio
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music-tag
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k-diffusion
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k-diffusion
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voicefixer
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@ -61,7 +61,7 @@ def tqdm_override(arr, verbose=False, progress=None, desc=None):
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if progress is None:
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return tqdm(arr, disable=not verbose)
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return progress.tqdm(arr, desc=desc, track_tqdm=True)
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return progress.tqdm(arr, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc, track_tqdm=True)
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def download_models(specific_models=None):
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"""
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@ -21,7 +21,7 @@ def tqdm_override(arr, verbose=False, progress=None, desc=None):
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if progress is None:
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return tqdm(arr, disable=not verbose)
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return progress.tqdm(arr, desc=desc, track_tqdm=True)
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return progress.tqdm(arr, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc, track_tqdm=True)
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def normal_kl(mean1, logvar1, mean2, logvar2):
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"""
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86
webui.py
86
webui.py
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@ -20,6 +20,8 @@ from tortoise.api import TextToSpeech
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from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
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from tortoise.utils.text import split_and_recombine_text
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voicefixer = None
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def generate(
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text,
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delimiter,
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@ -51,7 +53,6 @@ def generate(
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except NameError:
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raise gr.Error("TTS is still initializing...")
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if voice != "microphone":
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voices = [voice]
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else:
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@ -128,14 +129,17 @@ def generate(
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audio_cache = {}
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resampler = torchaudio.transforms.Resample(
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tts.output_sample_rate,
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args.output_sample_rate,
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lowpass_filter_width=16,
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rolloff=0.85,
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resampling_method="kaiser_window",
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beta=8.555504641634386,
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) if tts.output_sample_rate != args.output_sample_rate else None
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resample = None
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# not a ternary in the event for some reason I want to rely on librosa's upsampling interpolator rather than torchaudio's, for some reason
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if tts.output_sample_rate != args.output_sample_rate:
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resampler = torchaudio.transforms.Resample(
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tts.output_sample_rate,
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args.output_sample_rate,
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lowpass_filter_width=16,
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rolloff=0.85,
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resampling_method="kaiser_window",
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beta=8.555504641634386,
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)
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volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None
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@ -147,11 +151,10 @@ def generate(
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idx = idx + 1
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def get_name(line=0, candidate=0, combined=False):
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if combined:
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return f"{idx}_combined"
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name = f"{idx}"
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if len(texts) > 1:
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if combined:
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name = f"{name}_combined"
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elif len(texts) > 1:
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name = f"{name}_{line}"
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if candidates > 1:
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name = f"{name}_{candidate}"
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@ -164,12 +167,14 @@ def generate(
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else:
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cut_text = f"[I am really {emotion.lower()},] {cut_text}"
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print(f"[{str(line+1)}/{str(len(texts))}] Generating line: {cut_text}")
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{progress.msg_prefix} Generating line: {cut_text}")
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start_time = time.time()
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gen, additionals = tts.tts(cut_text, **settings )
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seed = additionals[0]
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run_time = time.time()-start_time
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print(f"Generating line took {run_time} seconds")
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if isinstance(gen, list):
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for j, g in enumerate(gen):
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@ -203,15 +208,11 @@ def generate(
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for candidate in range(candidates):
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audio_clips = []
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for line in range(len(texts)):
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if isinstance(gen, list):
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name = get_name(line=line, candidate=candidate)
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audio = audio_cache[name]['audio']
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else:
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name = get_name(line=line)
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audio = audio_cache[name]['audio']
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name = get_name(line=line, candidate=candidate)
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audio = audio_cache[name]['audio']
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audio_clips.append(audio)
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name = get_name(combined=True)
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name = get_name(candidate=candidate, combined=True)
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audio = torch.cat(audio_clips, dim=-1)
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torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)
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@ -225,16 +226,10 @@ def generate(
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output_voices.append(f'{outdir}/{voice}_{name}.wav')
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if output_voice is None:
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output_voice = f'{outdir}/{voice}_{name}.wav'
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# output_voice = audio
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else:
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if candidates > 1:
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for candidate in range(candidates):
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name = get_name(candidate=candidate)
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output_voices.append(f'{outdir}/{voice}_{name}.wav')
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else:
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name = get_name()
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for candidate in range(candidates):
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name = get_name(candidate=candidate)
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output_voices.append(f'{outdir}/{voice}_{name}.wav')
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#output_voice = f'{outdir}/{voice}_{name}.wav'
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info = {
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'text': text,
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@ -267,8 +262,21 @@ def generate(
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with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f:
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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if voicefixer:
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# we could do this on the pieces before they get stiched up anyways to save some compute
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# but the stitching would need to read back from disk, defeating the point of caching the waveform
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for path in progress.tqdm(audio_cache, desc="Running voicefix..."):
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print("VoiceFix starting")
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voicefixer.restore(
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input=f'{outdir}/{voice}_{k}.wav',
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output=f'{outdir}/{voice}_{k}.wav',
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#cuda=False,
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#mode=mode,
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)
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print("VoiceFix finished")
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if args.embed_output_metadata:
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for path in audio_cache:
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for path in progress.tqdm(audio_cache, desc="Embedding metadata..."):
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info['text'] = audio_cache[path]['text']
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info['time'] = audio_cache[path]['time']
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@ -438,7 +446,7 @@ def cancel_generate():
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def update_voices():
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return gr.Dropdown.update(choices=sorted(os.listdir(get_voice_dir())) + ["microphone"])
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def export_exec_settings( share, listen, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, cond_latent_max_chunk_size, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
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def export_exec_settings( share, listen, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, cond_latent_max_chunk_size, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
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args.share = share
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args.listen = listen
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args.low_vram = low_vram
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@ -448,6 +456,7 @@ def export_exec_settings( share, listen, check_for_updates, models_from_local_on
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args.sample_batch_size = sample_batch_size
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args.embed_output_metadata = embed_output_metadata
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args.latents_lean_and_mean = latents_lean_and_mean
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args.voice_fixer = voice_fixer
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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_volume = output_volume
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@ -462,6 +471,7 @@ def export_exec_settings( share, listen, check_for_updates, models_from_local_on
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'sample-batch-size': args.sample_batch_size,
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'embed-output-metadata': args.embed_output_metadata,
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'latents-lean-and-mean': args.latents_lean_and_mean,
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'voice-fixer': args.voice_fixer,
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'concurrency-count': args.concurrency_count,
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'output-sample-rate': args.output_sample_rate,
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'output-volume': args.output_volume,
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@ -480,6 +490,7 @@ def setup_args():
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'sample-batch-size': None,
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'embed-output-metadata': True,
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'latents-lean-and-mean': True,
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'voice-fixer': True,
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'cond-latent-max-chunk-size': 1000000,
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'concurrency-count': 2,
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'output-sample-rate': 44100,
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@ -500,6 +511,7 @@ def setup_args():
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parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
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parser.add_argument("--no-embed-output-metadata", action='store_false', default=not default_arguments['embed-output-metadata'], help="Disables embedding output metadata into resulting WAV files for easily fetching its settings used with the web UI (data is stored in the lyrics metadata tag)")
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parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
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parser.add_argument("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.")
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parser.add_argument("--cond-latent-max-chunk-size", default=default_arguments['cond-latent-max-chunk-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
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parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets an upper limit to audio chunk size when computing conditioning latents")
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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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@ -526,6 +538,17 @@ def setup_args():
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def setup_tortoise():
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global args
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global voicefixer
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if args.voice_fixer:
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try:
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from voicefixer import VoiceFixer
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print("Initializating voice-fixer")
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voicefixer = VoiceFixer()
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print("initialized voice-fixer")
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except Exception as e:
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pass
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print("Initializating TorToiSe...")
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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print("TorToiSe initialized, ready for generation.")
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@ -736,6 +759,7 @@ def setup_gradio():
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gr.Checkbox(label="Low VRAM", value=args.low_vram),
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gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata),
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gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean),
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gr.Checkbox(label="Voice Fixer", value=args.voice_fixer),
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]
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gr.Button(value="Check for Updates").click(check_for_updates)
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gr.Button(value="Reload TTS").click(reload_tts)
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