diff --git a/inference_demo.py b/inference_demo.py index 7e438ae..7c9f62e 100644 --- a/inference_demo.py +++ b/inference_demo.py @@ -9,7 +9,6 @@ from data.tokenizer import ( AudioTokenizer, TextTokenizer, ) -from IPython.display import display, Audio import argparse import random import numpy as np @@ -25,7 +24,7 @@ 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") + description="VoiceCraft TTS 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"], @@ -34,15 +33,15 @@ def parse_arguments(): 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_k", type=float, default=0, + help="Top-k value") parser.add_argument("--top_p", type=float, default=0.9, - help="Top-p sampling value") + help="Top-p 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, + parser.add_argument("--kvcache", type=int, default=1, choices=[0, 1], help="Key-value cache flag (0 or 1)") parser.add_argument("--stop_repetition", type=int, default=3, help="Stop repetition for generation") @@ -54,7 +53,6 @@ def parse_arguments(): help="beam size for MFA alignment") parser.add_argument("--retry_beam_size", type=int, default=40, help="retry beam size for MFA alignment") - parser.add_argument("--output_dir", type=str, default="./generated_tts", help="directory to save generated audio") parser.add_argument("--original_audio", type=str, @@ -147,9 +145,6 @@ 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) @@ -162,6 +157,7 @@ def seed_everything(seed): seed_everything(seed) +# inference 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( diff --git a/speech_editing_demo.py b/speech_editing_demo.py index 85e6ff1..220d342 100644 --- a/speech_editing_demo.py +++ b/speech_editing_demo.py @@ -1 +1,219 @@ -# WIP +""" +This script will allow you to run Speech Editing inference with Voicecraft +Before getting started, be sure to follow the environment setup. +""" + +from inference_speech_editing_scale import inference_one_sample, get_mask_interval +from edit_utils import get_span +from models import voicecraft +from data.tokenizer import ( + AudioTokenizer, + TextTokenizer, +) +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 Speech Editing: 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("--silence_tokens", type=int, nargs="*", + default=[1388, 1898, 131], help="Silence token IDs") + parser.add_argument("--left_margin", type=float, + default=0.08, help="Left margin value.") + parser.add_argument("--right_margin", type=float, + default=0.08, help="Right margin value.") + parser.add_argument("--codec_audio_sr", type=int, + default=16000, help="Codec audio sample rate.") + parser.add_argument("--codec_sr", type=int, default=50, + help="Codec sample rate.") + parser.add_argument("--top_k", type=float, default=0, help="Top k value.") + parser.add_argument("--top_p", type=float, + default=0.8, help="Top p value.") + parser.add_argument("--temperature", type=float, + default=1, help="Temperature value.") + parser.add_argument("--kvcache", type=float, + default=0, help="Kvcache value.") + parser.add_argument("--seed", type=int, default=1, help="Seed value.") + parser.add_argument("--beam_size", type=int, default=10, + help="beam size for MFA alignment") + parser.add_argument("--retry_beam_size", type=int, default=40, + help="retry beam size for MFA alignment") + parser.add_argument("--original_audio", type=str, + default="./demo/84_121550_000074_000000.wav", help="location of audio file") + parser.add_argument("--stop_repetition", type=int, + default=-1, help="Stop repetition for generation") + 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 transcript") + parser.add_argument("--target_transcript", type=str, + default="But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,", + help="target transcript") + parser.add_argument("--edit_type", type=str, + default="substitution", + choices=["insertion", "substitution", "deletion"], + help="type of specified edit") + parser.add_argument("--output_dir", type=str, + default="./demo/generated_se", help="output directory") + args = parser.parse_args() + return args + + +args = parse_arguments() + +voicecraft_name = args.model_name + +# hyperparameters for inference +left_margin = args.left_margin +right_margin = args.right_margin +codec_audio_sr = args.codec_audio_sr +codec_sr = args.codec_sr +top_k = args.top_k +top_p = args.top_p +temperature = args.temperature +kvcache = args.kvcache +# NOTE: adjust the below three arguments if the generation is not as good +seed = args.seed # random seed magic +silence_tokens = args.silence_tokens +# if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1 +stop_repetition = args.stop_repetition +# 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 + + +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) +device = "cuda" if torch.cuda.is_available() else "cpu" +# or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth +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) + +text_tokenizer = TextTokenizer(backend="espeak") + +# point to the original file or record the file +# 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.makedirs(align_temp, exist_ok=True) +beam_size = args.beam_size +retry_beam_size = args.retry_beam_size + +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 {beam_size} --retry_beam {retry_beam_size}" + ) +# if it fail, it could be because the audio is too hard for the alignment model, increasing the beam size usually solves the issue +# os.system(f"mfa align -j 1 --clean --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp} --beam 1000 --retry_beam 2000") +audio_fn = f"{temp_folder}/{filename}.wav" +transcript_fn = f"{temp_folder}/{filename}.txt" +align_fn = f"{align_temp}/{filename}.csv" + +# propose what do you want the target modified transcript to be +target_transcript = args.target_transcript +edit_type = args.edit_type + +# if you want to do a second modification on top of the first one, write down the second modification (target_transcript2, type_of_modification2) +# make sure the two modification do not overlap, if they do, you need to combine them into one modification + +# run the script to turn user input to the format that the model can take +orig_span, new_span = get_span(orig_transcript, target_transcript, edit_type) +if orig_span[0] > orig_span[1]: + RuntimeError(f"example {audio_fn} failed") +if orig_span[0] == orig_span[1]: + orig_span_save = [orig_span[0]] +else: + orig_span_save = orig_span +if new_span[0] == new_span[1]: + new_span_save = [new_span[0]] +else: + new_span_save = new_span + +orig_span_save = ",".join([str(item) for item in orig_span_save]) +new_span_save = ",".join([str(item) for item in new_span_save]) + +start, end = get_mask_interval(align_fn, orig_span_save, edit_type) +info = torchaudio.info(audio_fn) +audio_dur = info.num_frames / info.sample_rate +morphed_span = (max(start - left_margin, 1/codec_sr), + min(end + right_margin, audio_dur)) # in seconds + +# span in codec frames +mask_interval = [[round(morphed_span[0]*codec_sr), + round(morphed_span[1]*codec_sr)]] +mask_interval = torch.LongTensor(mask_interval) # [M,2], M==1 for now + +# run the model to get the output + +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} +orig_audio, new_audio = inference_one_sample(model, argparse.Namespace( + **config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, mask_interval, device, decode_config) + +# save segments for comparison +orig_audio, new_audio = orig_audio[0].cpu(), new_audio[0].cpu() +# logging.info(f"length of the resynthesize orig audio: {orig_audio.shape}") + +# save the audio +output_dir = args.output_dir +os.makedirs(output_dir, exist_ok=True) + +save_fn_new = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_new_seed{seed}.wav" + +torchaudio.save(save_fn_new, new_audio, codec_audio_sr) + +save_fn_orig = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_orig.wav" +if not os.path.isfile(save_fn_orig): + orig_audio, orig_sr = torchaudio.load(audio_fn) + if orig_sr != codec_audio_sr: + orig_audio = torchaudio.transforms.Resample( + orig_sr, codec_audio_sr)(orig_audio) + torchaudio.save(save_fn_orig, orig_audio, codec_audio_sr) + +# # if you get error importing T5 in transformers +# # try +# # pip uninstall Pillow +# # pip install Pillow +# # you are likely to get warning looks like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored