diff --git a/inference_demo.py b/inference_demo.py index 85e6ff1..bf14294 100644 --- a/inference_demo.py +++ b/inference_demo.py @@ -1 +1,184 @@ -# WIP +""" +This script will allow you to run TTS inference with Voicecraft +Before getting started, be sure to follow the environment setup. +""" + +from inference_tts_scale import inference_one_sample +from models import voicecraft +from data.tokenizer import ( + AudioTokenizer, + TextTokenizer, +) +from IPython.display import display, Audio +import argparse +import random +import numpy as np +import torchaudio +import torch +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = "0" +os.environ["USER"] = "me" # TODO change this to your username + +device = "cuda" if torch.cuda.is_available() else "cpu" + + +def parse_arguments(): + parser = argparse.ArgumentParser( + description="VoiceCraft Inference: see the script for more information on the options") + + parser.add_argument("--model_name", type=str, default="giga330M.pth", choices=[ + "giga330M.pth", "gigaHalfLibri330M_TTSEnhanced_max16s.pth", "giga830M.pth"], + help="VoiceCraft model to use") + parser.add_argument("--codec_audio_sr", type=int, + default=16000, help="Audio sampling rate for the codec") + parser.add_argument("--codec_sr", type=int, default=50, + help="Sampling rate for the codec") + parser.add_argument("--top_k", type=int, default=0, + help="Top-k sampling value") + parser.add_argument("--top_p", type=float, default=0.9, + help="Top-p sampling value") + parser.add_argument("--temperature", type=float, + default=1.0, help="Temperature for sampling") + parser.add_argument("--silence_tokens", type=int, nargs="*", + default=[1388, 1898, 131], help="Silence token IDs") + parser.add_argument("--kvcache", type=int, default=1, + help="Key-value cache flag (0 or 1)") + parser.add_argument("--stop_repetition", type=int, + default=3, help="Stop repetition for generation") + parser.add_argument("--sample_batch_size", type=int, + default=3, help="Batch size for sampling") + parser.add_argument("--seed", type=int, default=1, + help="Random seed for reproducibility") + parser.add_argument("--output_dir", type=str, default="./generated_tts", + help="directory to save generated audio") + parser.add_argument("--original_audio", type=str, + default="./demo/84_121550_000074_000000.wav", help="location of target audio file") + parser.add_argument("--original_transcript", type=str, + default="But when I had approached so near to them The common object, which the sense deceives, Lost not by distance any of its marks,", + help="original audio transcript") + parser.add_argument("--target_transcript", type=str, + default="Gwynplaine had, besides, for his work and for his feats of strength, I cannot believe that the same model can also do text to speech synthesis too!", + help="target audio transcript") + parser.add_argument("--cut_off_sec", type=float, default=3.6, + help="cut off point in seconds for input prompt") + args = parser.parse_args() + return args + + +args = parse_arguments() + +voicecraft_name = args.model_name +# hyperparameters for inference +codec_audio_sr = args.codec_audio_sr +codec_sr = args.codec_sr +top_k = args.top_k +top_p = args.top_p # defaults to 0.9 can also try 0.8, but 0.9 seems to work better +temperature = args.temperature +silence_tokens = args.silence_tokens +kvcache = args.kvcache # NOTE if OOM, change this to 0, or try the 330M model + +# NOTE adjust the below three arguments if the generation is not as good +# NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1 +stop_repetition = args.stop_repetition + +# NOTE: if the if there are long silence or unnaturally strecthed words, +# increase sample_batch_size to 4 or higher. What this will do to the model is that the +# model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. +# So if the speech rate of the generated is too fast change it to a smaller number. +sample_batch_size = args.sample_batch_size +seed = args.seed # change seed if you are still unhappy with the result + +# load the model +model = voicecraft.VoiceCraft.from_pretrained( + f"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}") +phn2num = model.args.phn2num +config = vars(model.args) +model.to(device) + +encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th" +if not os.path.exists(encodec_fn): + os.system( + f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th") + os.system( + f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th") +# will also put the neural codec model on gpu +audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) + +text_tokenizer = TextTokenizer(backend="espeak") + +# Prepare your audio +# point to the original audio whose speech you want to clone +# write down the transcript for the file, or run whisper to get the transcript (and you can modify it if it's not accurate), save it as a .txt file +orig_audio = args.original_audio +orig_transcript = args.original_transcript + +# move the audio and transcript to temp folder +temp_folder = "./demo/temp" +os.makedirs(temp_folder, exist_ok=True) +os.system(f"cp {orig_audio} {temp_folder}") +filename = os.path.splitext(orig_audio.split("/")[-1])[0] +with open(f"{temp_folder}/{filename}.txt", "w") as f: + f.write(orig_transcript) +# run MFA to get the alignment +align_temp = f"{temp_folder}/mfa_alignments" + +os.system("source ~/.bashrc && \ + conda activate voicecraft && \ + mfa align -v --clean -j 1 --output_format csv {temp_folder} \ + english_us_arpa english_us_arpa {align_temp}" + ) + +# # if the above fails, it could be because the audio is too hard for the alignment model, +# increasing the beam size usually solves the issue +# os.system("source ~/.bashrc && \ +# conda activate voicecraft && \ +# mfa align -v --clean -j 1 --output_format csv {temp_folder} \ +# english_us_arpa english_us_arpa {align_temp} --beam 1000 --retry_beam 2000") + +# take a look at demo/temp/mfa_alignment, decide which part of the audio to use as prompt +cut_off_sec = args.cut_off_sec # NOTE: according to forced-alignment file demo/temp/mfa_alignments/5895_34622_000026_000002.wav, the word "strength" stop as 3.561 sec, so we use first 3.6 sec as the prompt. this should be different for different audio +target_transcript = args.target_transcript +# NOTE: 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec. +audio_fn = f"{temp_folder}/{filename}.wav" +info = torchaudio.info(audio_fn) +audio_dur = info.num_frames / info.sample_rate + +assert cut_off_sec < audio_dur, f"cut_off_sec {cut_off_sec} is larger than the audio duration {audio_dur}" +prompt_end_frame = int(cut_off_sec * info.sample_rate) + +# run the model to get the output + + +def seed_everything(seed): + os.environ['PYTHONHASHSEED'] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + + +seed_everything(seed) + +decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache, + "codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr, "silence_tokens": silence_tokens, "sample_batch_size": sample_batch_size} +concated_audio, gen_audio = inference_one_sample(model, argparse.Namespace( + **config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame) + +# save segments for comparison +concated_audio, gen_audio = concated_audio[0].cpu(), gen_audio[0].cpu() +# logging.info(f"length of the resynthesize orig audio: {orig_audio.shape}") + +# save the audio +# output_dir +output_dir = args.output_dir +os.makedirs(output_dir, exist_ok=True) +seg_save_fn_gen = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_gen_seed{seed}.wav" +seg_save_fn_concat = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_concat_seed{seed}.wav" + +torchaudio.save(seg_save_fn_gen, gen_audio, codec_audio_sr) +torchaudio.save(seg_save_fn_concat, concated_audio, codec_audio_sr) + +# you might get warnings like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored