import argparse import os import torch import torchaudio from data.audio.unsupervised_audio_dataset import load_audio from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \ load_discrete_vocoder_diffuser, wav_to_mel, convert_mel_to_codes from utils.audio import plot_spectrogram from utils.util import load_model_from_config def ceil_multiple(base, multiple): res = base % multiple if res == 0: return base return base + (multiple - res) def get_ctc_codes_for(src_clip_path): """ Uses wav2vec2 to infer CTC codes for the audio clip at the specified path. """ from transformers import Wav2Vec2ForCTC from transformers import Wav2Vec2Processor model = Wav2Vec2ForCTC.from_pretrained(f"facebook/wav2vec2-large-960h").to("cuda") processor = Wav2Vec2Processor.from_pretrained(f"facebook/wav2vec2-large-960h") clip = load_audio(src_clip_path, 16000).squeeze() clip_inp = processor(clip.numpy(), return_tensors='pt', sampling_rate=16000).input_values.cuda() logits = model(clip_inp).logits return torch.argmax(logits, dim=-1), clip if __name__ == '__main__': provided_voices = { # Male 'simmons': 'Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav', 'carlin': 'Y:\\clips\\books1\\12_dchha13 Bubonic Nukes\\00097.wav', 'entangled': 'Y:\\clips\\books1\\3857_25_The_Entangled_Bank__000000000\\00123.wav', 'snowden': 'Y:\\clips\\books1\\7658_Edward_Snowden_-_Permanent_Record__000000004\\00027.wav', # Female 'the_doctor': 'Y:\\clips\\books2\\37062___The_Doctor__000000003\\00206.wav', 'puppy': 'Y:\\clips\\books2\\17830___3_Puppy_Kisses__000000002\\00046.wav', 'adrift': 'Y:\\clips\\books2\\5608_Gear__W_Michael_-_Donovan_1-5_(2018-2021)_(book_4_Gear__W_Michael_-_Donovan_5_-_Adrift_(2021)_Gear__W_Michael_-_Adrift_(Donovan_5)_—_82__000000000\\00019.wav', } parser = argparse.ArgumentParser() parser.add_argument('-src_clip', type=str, help='Path to the audio file to translate', default='D:\\tortoise-tts\\voices\\dotrice\\1.wav') parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium.yml') parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator') parser.add_argument('-diffusion_model_path', type=str, help='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\73000_generator_ema.pth') parser.add_argument('-sr_opt', type=str, help='Path to options YAML file used to train the SR diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample.yml') parser.add_argument('-sr_diffusion_model_name', type=str, help='Name of the SR diffusion model in opt.', default='generator') parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample_continued\\models\\41000_generator_ema.pth') parser.add_argument('-voice', type=str, help='Type of conditioning voice', default='puppy') parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100) parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_voice_translation') parser.add_argument('-device', type=str, help='Device to run on', default='cuda') args = parser.parse_args() os.makedirs(args.output_path, exist_ok=True) # Fixed parameters. base_sample_rate = 5500 sr_sample_rate = 22050 print("Loading Diffusion Models..") diffusion = load_model_from_config(args.opt, args.diffusion_model_name, also_load_savepoint=False, load_path=args.diffusion_model_path, device='cpu').eval() diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule='cosine') aligned_codes_compression_factor = base_sample_rate * 221 // 11025 sr_diffusion = load_model_from_config(args.sr_opt, args.sr_diffusion_model_name, also_load_savepoint=False, load_path=args.sr_diffusion_model_path, device='cpu').eval() sr_diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule='linear') sr_cond = load_audio(provided_voices[args.voice], sr_sample_rate).to(args.device) cond = torchaudio.functional.resample(sr_cond, sr_sample_rate, base_sample_rate) torchaudio.save(os.path.join(args.output_path, 'cond_base.wav'), cond.cpu(), base_sample_rate) torchaudio.save(os.path.join(args.output_path, 'cond_sr.wav'), sr_cond.cpu(), sr_sample_rate) with torch.no_grad(): print("Extracting CTC codes from source clip..") aligned_codes, src_clip = get_ctc_codes_for(args.src_clip) torchaudio.save(os.path.join(args.output_path, f'source_clip.wav'), src_clip.unsqueeze(0).cpu(), 16000) print("Performing initial diffusion..") output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048)) diffusion = diffusion.cuda() output_base = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device), model_kwargs={'tokens': aligned_codes, 'conditioning_input': cond.unsqueeze(0)}) diffusion = diffusion.cpu() torchaudio.save(os.path.join(args.output_path, f'output_mean_base.wav'), output_base.cpu().squeeze(0), base_sample_rate) print("Performing SR diffusion..") output_shape = (1, 1, output_base.shape[-1] * (sr_sample_rate // base_sample_rate)) sr_diffusion = sr_diffusion.cuda() output = sr_diffuser.p_sample_loop(sr_diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device), model_kwargs={'tokens': aligned_codes, 'conditioning_input': sr_cond.unsqueeze(0), 'lr_input': output_base}) sr_diffusion = sr_diffusion.cpu() torchaudio.save(os.path.join(args.output_path, f'output_mean_sr.wav'), output.cpu().squeeze(0), sr_sample_rate)