mirror of
https://github.com/jasonppy/VoiceCraft.git
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221 lines
9.7 KiB
Python
221 lines
9.7 KiB
Python
"""
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This script will allow you to run Speech Editing inference with Voicecraft
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Before getting started, be sure to follow the environment setup.
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"""
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from inference_speech_editing_scale import inference_one_sample, get_mask_interval
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from edit_utils import get_span
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from models import voicecraft
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from data.tokenizer import (
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AudioTokenizer,
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TextTokenizer,
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)
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import argparse
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import random
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import numpy as np
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import torchaudio
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import torch
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import os
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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os.environ["USER"] = "me" # TODO change this to your username
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device = "cuda" if torch.cuda.is_available() else "cpu"
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def parse_arguments():
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parser = argparse.ArgumentParser(
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description="VoiceCraft Speech Editing: see the script for more information on the options")
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parser.add_argument("-m", "--model_name", type=str, default="giga330M.pth", choices=[
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"giga330M.pth", "gigaHalfLibri330M_TTSEnhanced_max16s.pth", "giga830M.pth"],
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help="VoiceCraft model to use")
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parser.add_argument("-st", "--silence_tokens", type=int, nargs="*",
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default=[1388, 1898, 131], help="Silence token IDs")
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parser.add_argument("-lm", "--left_margin", type=float,
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default=0.08, help="Left margin value.")
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parser.add_argument("-rm", "--right_margin", type=float,
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default=0.08, help="Right margin value.")
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parser.add_argument("-casr", "--codec_audio_sr", type=int,
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default=16000, help="Codec audio sample rate.")
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parser.add_argument("-csr", "--codec_sr", type=int, default=50,
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help="Codec sample rate.")
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parser.add_argument("-k", "--top_k", type=float,
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default=0, help="Top k value.")
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parser.add_argument("-p", "--top_p", type=float,
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default=0.8, help="Top p value.")
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parser.add_argument("-t", "--temperature", type=float,
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default=1, help="Temperature value.")
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parser.add_argument("-kv", "--kvcache", type=float, choices=[0, 1],
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default=0, help="Kvcache value.")
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parser.add_argument("-sr", "--stop_repetition", type=int,
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default=-1, help="Stop repetition for generation")
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parser.add_argument("-s", "--seed", type=int, default=1, help="Seed value.")
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parser.add_argument("-bs", "--beam_size", type=int, default=10,
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help="beam size for MFA alignment")
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parser.add_argument("-rbs", "--retry_beam_size", type=int, default=40,
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help="retry beam size for MFA alignment")
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parser.add_argument("-oa", "--original_audio", type=str,
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default="./demo/84_121550_000074_000000.wav", help="location of audio file")
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parser.add_argument("-ot", "--original_transcript", type=str,
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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,",
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help="original transcript")
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parser.add_argument("-tt", "--target_transcript", type=str,
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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,",
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help="target transcript")
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parser.add_argument("-et", "--edit_type", type=str,
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default="substitution",
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choices=["insertion", "substitution", "deletion"],
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help="type of specified edit")
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parser.add_argument("-o", "--output_dir", type=str,
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default="./demo/generated_se", help="output directory")
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args = parser.parse_args()
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return args
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args = parse_arguments()
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voicecraft_name = args.model_name
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# hyperparameters for inference
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left_margin = args.left_margin
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right_margin = args.right_margin
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codec_audio_sr = args.codec_audio_sr
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codec_sr = args.codec_sr
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top_k = args.top_k
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top_p = args.top_p
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temperature = args.temperature
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kvcache = args.kvcache
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# NOTE: adjust the below three arguments if the generation is not as good
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seed = args.seed # random seed magic
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silence_tokens = args.silence_tokens
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# if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1
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stop_repetition = args.stop_repetition
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# 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
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def seed_everything(seed):
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os.environ['PYTHONHASHSEED'] = str(seed)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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seed_everything(seed)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth
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model = voicecraft.VoiceCraft.from_pretrained(
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f"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}")
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phn2num = model.args.phn2num
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config = vars(model.args)
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model.to(device)
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encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
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if not os.path.exists(encodec_fn):
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os.system(
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f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
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os.system(
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f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
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# will also put the neural codec model on gpu
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audio_tokenizer = AudioTokenizer(signature=encodec_fn)
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text_tokenizer = TextTokenizer(backend="espeak")
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# point to the original file or record the file
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# 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
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orig_audio = args.original_audio
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orig_transcript = args.original_transcript
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# move the audio and transcript to temp folder
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temp_folder = "./demo/temp"
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os.makedirs(temp_folder, exist_ok=True)
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os.system(f"cp {orig_audio} {temp_folder}")
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filename = os.path.splitext(orig_audio.split("/")[-1])[0]
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with open(f"{temp_folder}/{filename}.txt", "w") as f:
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f.write(orig_transcript)
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# run MFA to get the alignment
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align_temp = f"{temp_folder}/mfa_alignments"
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os.makedirs(align_temp, exist_ok=True)
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beam_size = args.beam_size
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retry_beam_size = args.retry_beam_size
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os.system("source ~/.bashrc && \
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conda activate voicecraft && \
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mfa align -v --clean -j 1 --output_format csv {temp_folder} \
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english_us_arpa english_us_arpa {align_temp} --beam {beam_size} --retry_beam {retry_beam_size}"
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)
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# if it fail, it could be because the audio is too hard for the alignment model, increasing the beam size usually solves the issue
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# 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")
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audio_fn = f"{temp_folder}/{filename}.wav"
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transcript_fn = f"{temp_folder}/{filename}.txt"
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align_fn = f"{align_temp}/{filename}.csv"
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# propose what do you want the target modified transcript to be
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target_transcript = args.target_transcript
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edit_type = args.edit_type
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# if you want to do a second modification on top of the first one, write down the second modification (target_transcript2, type_of_modification2)
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# make sure the two modification do not overlap, if they do, you need to combine them into one modification
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# run the script to turn user input to the format that the model can take
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orig_span, new_span = get_span(orig_transcript, target_transcript, edit_type)
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if orig_span[0] > orig_span[1]:
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RuntimeError(f"example {audio_fn} failed")
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if orig_span[0] == orig_span[1]:
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orig_span_save = [orig_span[0]]
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else:
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orig_span_save = orig_span
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if new_span[0] == new_span[1]:
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new_span_save = [new_span[0]]
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else:
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new_span_save = new_span
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orig_span_save = ",".join([str(item) for item in orig_span_save])
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new_span_save = ",".join([str(item) for item in new_span_save])
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start, end = get_mask_interval(align_fn, orig_span_save, edit_type)
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info = torchaudio.info(audio_fn)
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audio_dur = info.num_frames / info.sample_rate
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morphed_span = (max(start - left_margin, 1/codec_sr),
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min(end + right_margin, audio_dur)) # in seconds
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# span in codec frames
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mask_interval = [[round(morphed_span[0]*codec_sr),
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round(morphed_span[1]*codec_sr)]]
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mask_interval = torch.LongTensor(mask_interval) # [M,2], M==1 for now
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# run the model to get the output
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decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition,
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'kvcache': kvcache, "codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr, "silence_tokens": silence_tokens}
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orig_audio, new_audio = inference_one_sample(model, argparse.Namespace(
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**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, mask_interval, device, decode_config)
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# save segments for comparison
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orig_audio, new_audio = orig_audio[0].cpu(), new_audio[0].cpu()
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# logging.info(f"length of the resynthesize orig audio: {orig_audio.shape}")
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# save the audio
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output_dir = args.output_dir
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os.makedirs(output_dir, exist_ok=True)
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save_fn_new = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_new_seed{seed}.wav"
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torchaudio.save(save_fn_new, new_audio, codec_audio_sr)
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save_fn_orig = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_orig.wav"
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if not os.path.isfile(save_fn_orig):
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orig_audio, orig_sr = torchaudio.load(audio_fn)
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if orig_sr != codec_audio_sr:
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orig_audio = torchaudio.transforms.Resample(
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orig_sr, codec_audio_sr)(orig_audio)
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torchaudio.save(save_fn_orig, orig_audio, codec_audio_sr)
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# # if you get error importing T5 in transformers
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# # try
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# # pip uninstall Pillow
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# # pip install Pillow
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# # 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
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