Added new options: "Output Sample Rate", "Output Volume", and documentation
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@ -197,6 +197,8 @@ Below are settings that override the default launch arguments. Some of these req
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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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* `Output Sample Rate`: the sample rate to save the generated audio as. It provides a bit of slight bump in quality
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* `Output Volume`: adjusts the volume through amplitude scaling
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Below are an explanation of experimental flags. Messing with these might impact performance, as these are exposed only if you know what you are doing.
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* `Half-Precision`: (attempts to) hint to PyTorch to auto-cast to float16 (half precision) for compute. Disabled by default, due to it making computations slower.
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137
app.py
137
app.py
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@ -21,6 +21,12 @@ from tortoise.utils.audio import load_audio, load_voice, load_voices
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from tortoise.utils.text import split_and_recombine_text
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def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, experimentals, progress=gr.Progress(track_tqdm=True)):
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try:
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tts
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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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@ -36,7 +42,8 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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voice_samples, conditioning_latents = load_voice(voice)
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if voice_samples is not None:
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sample_voice = voice_samples[0]
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sample_voice = voice_samples[0].squeeze().cpu()
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, max_chunk_size=args.cond_latent_max_chunk_size)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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@ -54,7 +61,6 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
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cvvp_weight = 0
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start_time = time.time()
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settings = {
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'temperature': temperature, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
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@ -86,14 +92,24 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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else:
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texts = split_and_recombine_text(text)
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start_time = time.time()
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timestamp = int(time.time())
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outdir = f"./results/{voice}/{timestamp}/"
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outdir = f"./results/{voice}/{int(start_time)}/"
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os.makedirs(outdir, exist_ok=True)
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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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volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None
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for line, cut_text in enumerate(texts):
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if emotion == "Custom":
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if prompt.strip() != "":
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@ -108,21 +124,27 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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if isinstance(gen, list):
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for j, g in enumerate(gen):
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audio = g.squeeze(0).cpu()
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os.makedirs(f'{outdir}/candidate_{j}', exist_ok=True)
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audio_cache[f"candidate_{j}/result_{line}.wav"] = {
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'audio': audio,
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'audio': g,
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'text': cut_text,
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}
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os.makedirs(f'{outdir}/candidate_{j}', exist_ok=True)
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torchaudio.save(f'{outdir}/candidate_{j}/result_{line}.wav', audio, tts.output_sample_rate)
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else:
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audio = gen.squeeze(0).cpu()
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audio_cache[f"result_{line}.wav"] = {
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'audio': audio,
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'audio': gen,
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'text': cut_text,
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}
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torchaudio.save(f'{outdir}/result_{line}.wav', audio, tts.output_sample_rate)
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for k in audio_cache:
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audio = audio_cache[k]['audio'].squeeze(0).cpu()
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if resampler is not None:
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audio = resampler(audio)
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if volume_adjust is not None:
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audio = volume_adjust(audio)
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audio_cache[k]['audio'] = audio
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torchaudio.save(f'{outdir}/{k}', audio, args.output_sample_rate)
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output_voice = None
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if len(texts) > 1:
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@ -136,7 +158,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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audio_clips.append(audio)
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audio = torch.cat(audio_clips, dim=-1)
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torchaudio.save(f'{outdir}/combined_{candidate}.wav', audio, tts.output_sample_rate)
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torchaudio.save(f'{outdir}/combined_{candidate}.wav', audio, args.output_sample_rate)
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audio = audio.squeeze(0).cpu()
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audio_cache[f'combined_{candidate}.wav'] = {
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@ -145,15 +167,15 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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}
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if output_voice is None:
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output_voice = audio
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output_voice = f'{outdir}/combined_{candidate}.wav'
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# output_voice = audio
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else:
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if isinstance(gen, list):
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output_voice = gen[0]
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output_voice = f'{outdir}/candidate_0/result_0.wav'
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#output_voice = gen[0]
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else:
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output_voice = gen
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if output_voice is not None:
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output_voice = (tts.output_sample_rate, output_voice.numpy())
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output_voice = f'{outdir}/result_0.wav'
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#output_voice = gen
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info = {
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'text': text,
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@ -188,9 +210,12 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
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metadata = music_tag.load_file(f"{outdir}/{path}")
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metadata['lyrics'] = json.dumps(info)
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metadata.save()
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#if output_voice is not None:
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# output_voice = (args.output_sample_rate, output_voice.numpy())
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if sample_voice is not None:
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sample_voice = (tts.input_sample_rate, sample_voice.squeeze().cpu().numpy())
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sample_voice = (tts.input_sample_rate, sample_voice.numpy())
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print(f"Generation took {info['time']} seconds, saved to '{outdir}'\n")
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@ -319,10 +344,13 @@ def check_for_updates():
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return False
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def reload_tts():
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tts = setup_tortoise()
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def update_voices():
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return gr.Dropdown.update(choices=sorted(os.listdir("./tortoise/voices")) + ["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 ):
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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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args.share = share
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args.listen = listen
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args.low_vram = low_vram
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@ -333,6 +361,8 @@ def export_exec_settings( share, listen, check_for_updates, models_from_local_on
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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.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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settings = {
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'share': args.share,
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@ -345,6 +375,8 @@ def export_exec_settings( share, listen, check_for_updates, models_from_local_on
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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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'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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}
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with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
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@ -361,7 +393,9 @@ def setup_args():
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'embed-output-metadata': True,
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'latents-lean-and-mean': True,
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'cond-latent-max-chunk-size': 1000000,
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'concurrency-count': 3,
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'concurrency-count': 2,
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'output-sample-rate': 44100,
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'output-volume': 1,
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}
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if os.path.isfile('./config/exec.json'):
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@ -381,6 +415,8 @@ def setup_args():
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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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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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args = parser.parse_args()
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args.embed_output_metadata = not args.no_embed_output_metadata
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@ -392,7 +428,7 @@ def setup_args():
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match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0]
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args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
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args.listen_port = match[1] if match[1] != "" else 8000
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args.listen_port = match[1] if match[1] != "" else None
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args.listen_path = match[2] if match[2] != "" else "/"
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if args.listen_port is not None:
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@ -516,34 +552,37 @@ def setup_gradio():
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)
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with gr.Tab("Settings"):
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with gr.Row():
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exec_inputs = []
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with gr.Column():
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with gr.Box():
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exec_arg_listen = gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/")
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exec_arg_share = gr.Checkbox(label="Public Share Gradio", value=args.share)
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exec_arg_check_for_updates = gr.Checkbox(label="Check For Updates", value=args.check_for_updates)
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exec_arg_models_from_local_only = gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only)
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exec_arg_low_vram = gr.Checkbox(label="Low VRAM", value=args.low_vram)
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exec_arg_embed_output_metadata = gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata)
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exec_arg_latents_lean_and_mean = gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean)
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exec_arg_cond_latent_max_chunk_size = gr.Number(label="Voice Latents Max Chunk Size", precision=0, value=args.cond_latent_max_chunk_size)
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exec_arg_sample_batch_size = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size)
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exec_arg_concurrency_count = gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count)
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exec_inputs = exec_inputs + [
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gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/"),
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gr.Checkbox(label="Public Share Gradio", value=args.share),
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gr.Checkbox(label="Check For Updates", value=args.check_for_updates),
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gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only),
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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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]
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gr.Button(value="Check for Updates").click(check_for_updates)
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with gr.Column():
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exec_inputs = exec_inputs + [
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gr.Number(label="Voice Latents Max Chunk Size", precision=0, value=args.cond_latent_max_chunk_size),
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gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
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gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count),
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gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate),
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gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume),
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]
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for i in exec_inputs:
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i.change(
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fn=export_exec_settings,
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inputs=exec_inputs
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)
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with gr.Column():
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experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
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check_updates_now = gr.Button(value="Check for Updates")
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exec_inputs = [exec_arg_share, exec_arg_listen, exec_arg_check_for_updates, exec_arg_models_from_local_only, exec_arg_low_vram, exec_arg_embed_output_metadata, exec_arg_latents_lean_and_mean, exec_arg_cond_latent_max_chunk_size, exec_arg_sample_batch_size, exec_arg_concurrency_count]
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for i in exec_inputs:
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i.change(
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fn=export_exec_settings,
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inputs=exec_inputs
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)
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check_updates_now.click(check_for_updates)
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gr.Button(value="Reload TTS").click(reload_tts)
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input_settings = [
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text,
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@ -591,7 +630,7 @@ if __name__ == "__main__":
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if args.listen_path is not None and args.listen_path != "/":
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import uvicorn
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uvicorn.run("app:app", host=args.listen_host, port=args.listen_port)
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uvicorn.run("app:app", host=args.listen_host, port=args.listen_port if not None else 8000)
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else:
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webui = setup_gradio()
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webui.launch(share=args.share, prevent_thread_lock=True, server_name=args.listen_host, server_port=args.listen_port)
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@ -562,7 +562,7 @@ class TextToSpeech:
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# results, but will increase memory usage.
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if not self.minor_optimizations:
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self.autoregressive = self.autoregressive.to(self.device)
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if get_device_name() == "dml":
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text_tokens = text_tokens.cpu()
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best_results = best_results.cpu()
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