Added choices to choose between diffusion samplers (p, ddim)
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32
app.py
32
app.py
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@ -9,7 +9,7 @@ from datetime import datetime
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from tortoise.api import TextToSpeech
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from tortoise.utils.audio import load_audio, load_voice, load_voices
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def inference(text, emotion, prompt, voice, mic_audio, preset, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, progress=gr.Progress()):
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def inference(text, emotion, prompt, voice, mic_audio, preset, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, progress=gr.Progress()):
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if voice != "microphone":
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voices = [voice]
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else:
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@ -42,11 +42,11 @@ def inference(text, emotion, prompt, voice, mic_audio, preset, seed, candidates,
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start_time = time.time()
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presets = {
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'ultra_fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
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'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
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'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
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'high_quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
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'none': {'num_autoregressive_samples': num_autoregressive_samples, 'diffusion_iterations': diffusion_iterations},
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'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
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'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
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'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
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'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
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'None': {'num_autoregressive_samples': num_autoregressive_samples, 'diffusion_iterations': diffusion_iterations},
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}
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settings = {
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'temperature': temperature, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
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@ -58,13 +58,14 @@ def inference(text, emotion, prompt, voice, mic_audio, preset, seed, candidates,
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'use_deterministic_seed': seed,
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'return_deterministic_state': True,
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'k': candidates,
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'diffusion_sampler': diffusion_sampler,
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'progress': progress,
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}
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settings.update(presets[preset])
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gen, additionals = tts.tts( text, **settings )
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seed = additionals[0]
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info = f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} preset / {num_autoregressive_samples} samples / {diffusion_iterations} iterations | Temperature: {temperature} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n"
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info = f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} preset / {num_autoregressive_samples} samples / {diffusion_iterations} iterations | Temperature: {temperature} | Diffusion Sampler: {diffusion_sampler} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n"
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with open("results.log", "a") as f:
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f.write(info)
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@ -74,7 +75,7 @@ def inference(text, emotion, prompt, voice, mic_audio, preset, seed, candidates,
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os.makedirs(outdir, exist_ok=True)
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with open(os.path.join(outdir, f'input.txt'), 'w') as f:
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f.write(f"{text}\n\n{info}")
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f.write(f"{info}")
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if isinstance(gen, list):
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for j, g in enumerate(gen):
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@ -104,10 +105,10 @@ def main():
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label="Emotion",
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type="value",
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)
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prompt = gr.Textbox(lines=1, label="Custom Emotion (if selected)")
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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preset = gr.Radio(
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["ultra_fast", "fast", "standard", "high_quality", "none"],
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value="none",
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["Ultra Fast", "Fast", "Standard", "High Quality", "None"],
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value="None",
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label="Preset",
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type="value",
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)
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@ -115,6 +116,12 @@ def main():
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num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
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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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diffusion_sampler = gr.Radio(
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["P", "DDIM"],
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value="P",
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label="Diffusion Samplers",
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type="value",
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)
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voice = gr.Dropdown(
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os.listdir(os.path.join("tortoise", "voices")) + ["random", "microphone", "disabled"],
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@ -145,7 +152,8 @@ def main():
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candidates,
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num_autoregressive_samples,
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diffusion_iterations,
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temperature
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temperature,
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diffusion_sampler
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],
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outputs=[selected_voice, output_audio, usedSeed],
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allow_flagging='never'
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@ -153,7 +153,7 @@ def fix_autoregressive_output(codes, stop_token, complain=True):
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return codes
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def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True, progress=None, desc=None):
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def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True, progress=None, desc=None, sampler="P"):
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"""
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Uses the specified diffusion model to convert discrete codes into a spectrogram.
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"""
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@ -163,9 +163,18 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_la
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precomputed_embeddings = diffusion_model.timestep_independent(latents, conditioning_latents, output_seq_len, False)
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noise = torch.randn(output_shape, device=latents.device) * temperature
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mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
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mel = None
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print(f"Sampler: {sampler}")
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if sampler == "P":
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mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
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model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
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verbose=verbose, progress=progress, desc=desc)
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elif sampler == "DDIM":
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mel = diffuser.ddim_sample_loop(diffusion_model, output_shape, noise=noise,
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model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
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verbose=verbose, progress=progress, desc=desc)
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return denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
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@ -361,6 +370,7 @@ class TextToSpeech:
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cvvp_amount=.0,
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# diffusion generation parameters follow
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diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=1.0,
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diffusion_sampler="P",
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progress=None,
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**hf_generate_kwargs):
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"""
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@ -531,7 +541,7 @@ class TextToSpeech:
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break
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mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, diffusion_conditioning,
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temperature=diffusion_temperature, verbose=verbose, progress=progress, desc="Transforming autoregressive outputs into audio..")
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temperature=diffusion_temperature, verbose=verbose, progress=progress, desc="Transforming autoregressive outputs into audio..", sampler=diffusion_sampler)
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wav = self.vocoder.inference(mel)
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wav_candidates.append(wav.cpu())
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@ -734,6 +734,7 @@ class GaussianDiffusion:
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verbose=False,
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eta=0.0,
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progress=None,
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desc=None,
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):
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"""
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Generate samples from the model using DDIM.
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@ -753,6 +754,7 @@ class GaussianDiffusion:
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verbose=verbose,
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eta=eta,
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progress=progress,
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desc=desc
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):
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final = sample
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return final["sample"]
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@ -770,6 +772,7 @@ class GaussianDiffusion:
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verbose=False,
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eta=0.0,
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progress=None,
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desc=None,
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):
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"""
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Use DDIM to sample from the model and yield intermediate samples from
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@ -790,7 +793,7 @@ class GaussianDiffusion:
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# Lazy import so that we don't depend on tqdm.
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from tqdm.auto import tqdm
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indices = tqdm_override(indices, verbose=verbose, desc="DDIM Sample Loop Progressive", progress=progress)
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indices = tqdm_override(indices, verbose=verbose, desc=desc, progress=progress)
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for i in indices:
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t = th.tensor([i] * shape[0], device=device)
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