diff --git a/app.py b/app.py index 0351aa0..b6aaa7a 100755 --- a/app.py +++ b/app.py @@ -27,7 +27,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate if voice == "microphone": if mic_audio is None: raise gr.Error("Please provide audio from mic when choosing `microphone` as a voice input") - mic = load_audio(mic_audio, 22050) + mic = load_audio(mic_audio, tts.input_sample_rate) voice_samples, conditioning_latents = [mic], None else: progress(0, desc="Loading voice...") @@ -105,14 +105,14 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate } os.makedirs(f'{outdir}/candidate_{j}', exist_ok=True) - torchaudio.save(f'{outdir}/candidate_{j}/result_{line}.wav', audio, 24000) + torchaudio.save(f'{outdir}/candidate_{j}/result_{line}.wav', audio, tts.output_sample_rate) else: audio = gen.squeeze(0).cpu() audio_cache[f"result_{line}.wav"] = { 'audio': audio, 'text': cut_text, } - torchaudio.save(f'{outdir}/result_{line}.wav', audio, 24000) + torchaudio.save(f'{outdir}/result_{line}.wav', audio, tts.output_sample_rate) output_voice = None if len(texts) > 1: @@ -126,7 +126,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate audio_clips.append(audio) audio = torch.cat(audio_clips, dim=-1) - torchaudio.save(f'{outdir}/combined_{candidate}.wav', audio, 24000) + torchaudio.save(f'{outdir}/combined_{candidate}.wav', audio, tts.output_sample_rate) audio = audio.squeeze(0).cpu() audio_cache[f'combined_{candidate}.wav'] = { @@ -143,7 +143,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate output_voice = gen if output_voice is not None: - output_voice = (24000, output_voice.numpy()) + output_voice = (tts.output_sample_rate, output_voice.numpy()) info = { 'text': text, @@ -179,7 +179,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate metadata.save() if sample_voice is not None: - sample_voice = (22050, sample_voice.squeeze().cpu().numpy()) + sample_voice = (tts.input_sample_rate, sample_voice.squeeze().cpu().numpy()) print(f"Generation took {info['time']} seconds, saved to '{outdir}'\n") @@ -514,6 +514,8 @@ if __name__ == "__main__": args = parser.parse_args() print("Initializating TorToiSe...") - tts = TextToSpeech(minor_optimizations=not args.low_vram) + tts = TextToSpeech( + minor_optimizations=not args.low_vram, + ) main() \ No newline at end of file diff --git a/tortoise/api.py b/tortoise/api.py index 315d61e..27c72dd 100755 --- a/tortoise/api.py +++ b/tortoise/api.py @@ -114,7 +114,7 @@ def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusi conditioning_free=cond_free, conditioning_free_k=cond_free_k) -def format_conditioning(clip, cond_length=132300, device='cuda'): +def format_conditioning(clip, cond_length=132300, device='cuda', sampling_rate=22050): """ Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models. """ @@ -124,7 +124,7 @@ def format_conditioning(clip, cond_length=132300, device='cuda'): elif gap > 0: rand_start = random.randint(0, gap) clip = clip[:, rand_start:rand_start + cond_length] - mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0) + mel_clip = TorchMelSpectrogram(sampling_rate=sample_rate)(clip.unsqueeze(0)).squeeze(0) return mel_clip.unsqueeze(0).to(device) @@ -158,12 +158,12 @@ def fix_autoregressive_output(codes, stop_token, complain=True): return codes -def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True, progress=None, desc=None, sampler="P"): +def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True, progress=None, desc=None, sampler="P", input_sample_rate=22050, output_sample_rate=24000): """ Uses the specified diffusion model to convert discrete codes into a spectrogram. """ with torch.no_grad(): - output_seq_len = latents.shape[1] * 4 * 24000 // 22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal. + output_seq_len = latents.shape[1] * 4 * output_sample_rate // input_sample_rate # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal. output_shape = (latents.shape[0], 100, output_seq_len) precomputed_embeddings = diffusion_model.timestep_independent(latents, conditioning_latents, output_seq_len, False) @@ -214,7 +214,7 @@ class TextToSpeech: Main entry point into Tortoise. """ - def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None, minor_optimizations=True): + def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None, minor_optimizations=True, input_sample_rate=22050, output_sample_rate=24000): """ Constructor :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing @@ -234,7 +234,10 @@ class TextToSpeech: if device is None: device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.input_sample_rate = input_sample_rate + self.output_sample_rate = output_sample_rate self.minor_optimizations = minor_optimizations + self.models_dir = models_dir self.autoregressive_batch_size = pick_best_batch_size_for_gpu() if autoregressive_batch_size is None or autoregressive_batch_size == 0 else autoregressive_batch_size self.enable_redaction = enable_redaction @@ -306,7 +309,7 @@ class TextToSpeech: if not isinstance(voice_samples, list): voice_samples = [voice_samples] for vs in voice_samples: - auto_conds.append(format_conditioning(vs, device=self.device)) + auto_conds.append(format_conditioning(vs, device=self.device, sampling_rate=self.input_sample_rate)) auto_conds = torch.stack(auto_conds, dim=1) @@ -315,7 +318,8 @@ class TextToSpeech: samples = [] # resample in its own pass to make things easier for sample in voice_samples: # The diffuser operates at a sample rate of 24000 (except for the latent inputs) - samples.append(torchaudio.functional.resample(sample, 22050, 24000)) + #samples.append(torchaudio.functional.resample(sample, 22050, 24000)) + samples.append(torchaudio.functional.resample(sample, self.input_sample_rate, self.output_sample_rate)) if chunk_size is None: for sample in tqdm_override(samples, verbose=verbose and len(samples) > 1, progress=progress if len(samples) > 1 else None, desc="Calculating size of best fit..."): @@ -582,7 +586,8 @@ class TextToSpeech: break mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, diffusion_conditioning, - temperature=diffusion_temperature, verbose=verbose, progress=progress, desc="Transforming autoregressive outputs into audio..", sampler=diffusion_sampler) + temperature=diffusion_temperature, verbose=verbose, progress=progress, desc="Transforming autoregressive outputs into audio..", sampler=diffusion_sampler, + input_sample_rate=self.input_sample_rate, output_sample_rate=self.output_sample_rate) wav = self.vocoder.inference(mel) wav_candidates.append(wav.cpu()) @@ -592,7 +597,7 @@ class TextToSpeech: def potentially_redact(clip, text): if self.enable_redaction: - return self.aligner.redact(clip.squeeze(1), text).unsqueeze(1) + return self.aligner.redact(clip.squeeze(1), text, self.output_sample_rate).unsqueeze(1) return clip wav_candidates = [potentially_redact(wav_candidate, text) for wav_candidate in wav_candidates] diff --git a/tortoise/utils/audio.py b/tortoise/utils/audio.py index e7cd3a8..82bbc9f 100755 --- a/tortoise/utils/audio.py +++ b/tortoise/utils/audio.py @@ -97,7 +97,7 @@ def get_voices(extra_voice_dirs=[]): return voices -def load_voice(voice, extra_voice_dirs=[], load_latents=True): +def load_voice(voice, extra_voice_dirs=[], load_latents=True, sample_rate=22050): if voice == 'random': return None, None @@ -125,7 +125,7 @@ def load_voice(voice, extra_voice_dirs=[], load_latents=True): conds = [] for cond_path in voices: - c = load_audio(cond_path, 22050) + c = load_audio(cond_path, sample_rate) conds.append(c) return conds, None @@ -197,8 +197,8 @@ class TacotronSTFT(torch.nn.Module): return mel_output -def wav_to_univnet_mel(wav, do_normalization=False, device='cuda'): - stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000) +def wav_to_univnet_mel(wav, do_normalization=False, device='cuda', sample_rate=24000): + stft = TacotronSTFT(1024, 256, 1024, 100, sample_rate, 0, 12000) stft = stft.to(device) mel = stft.mel_spectrogram(wav) if do_normalization: