add TTS
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Pranay Gosar
parent
fc4de13071
commit
63736f7269
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# WIP
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"""
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This script will allow you to run TTS 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_tts_scale import inference_one_sample
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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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from IPython.display import display, Audio
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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 Inference: see the script for more information on the options")
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parser.add_argument("--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("--codec_audio_sr", type=int,
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default=16000, help="Audio sampling rate for the codec")
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parser.add_argument("--codec_sr", type=int, default=50,
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help="Sampling rate for the codec")
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parser.add_argument("--top_k", type=int, default=0,
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help="Top-k sampling value")
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parser.add_argument("--top_p", type=float, default=0.9,
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help="Top-p sampling value")
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parser.add_argument("--temperature", type=float,
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default=1.0, help="Temperature for sampling")
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parser.add_argument("--silence_tokens", type=int, nargs="*",
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default=[1388, 1898, 131], help="Silence token IDs")
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parser.add_argument("--kvcache", type=int, default=1,
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help="Key-value cache flag (0 or 1)")
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parser.add_argument("--stop_repetition", type=int,
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default=3, help="Stop repetition for generation")
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parser.add_argument("--sample_batch_size", type=int,
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default=3, help="Batch size for sampling")
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parser.add_argument("--seed", type=int, default=1,
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help="Random seed for reproducibility")
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parser.add_argument("--output_dir", type=str, default="./generated_tts",
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help="directory to save generated audio")
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parser.add_argument("--original_audio", type=str,
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default="./demo/84_121550_000074_000000.wav", help="location of target audio file")
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parser.add_argument("--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 audio transcript")
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parser.add_argument("--target_transcript", type=str,
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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!",
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help="target audio transcript")
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parser.add_argument("--cut_off_sec", type=float, default=3.6,
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help="cut off point in seconds for input prompt")
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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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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 # defaults to 0.9 can also try 0.8, but 0.9 seems to work better
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temperature = args.temperature
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silence_tokens = args.silence_tokens
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kvcache = args.kvcache # NOTE if OOM, change this to 0, or try the 330M model
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# NOTE adjust the below three arguments if the generation is not as good
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# NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1
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stop_repetition = args.stop_repetition
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# NOTE: if the if there are long silence or unnaturally strecthed words,
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# increase sample_batch_size to 4 or higher. What this will do to the model is that the
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# model will run sample_batch_size examples of the same audio, and pick the one that's the shortest.
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# So if the speech rate of the generated is too fast change it to a smaller number.
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sample_batch_size = args.sample_batch_size
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seed = args.seed # change seed if you are still unhappy with the result
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# load the model
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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, device=device)
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text_tokenizer = TextTokenizer(backend="espeak")
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# Prepare your audio
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# point to the original audio whose speech you want to clone
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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.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}"
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)
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# # if the above fails, it could be because the audio is too hard for the alignment model,
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# increasing the beam size usually solves the issue
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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 1000 --retry_beam 2000")
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# take a look at demo/temp/mfa_alignment, decide which part of the audio to use as prompt
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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
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target_transcript = args.target_transcript
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# NOTE: 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec.
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audio_fn = f"{temp_folder}/{filename}.wav"
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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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assert cut_off_sec < audio_dur, f"cut_off_sec {cut_off_sec} is larger than the audio duration {audio_dur}"
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prompt_end_frame = int(cut_off_sec * info.sample_rate)
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# run the model to get the output
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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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decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache,
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"codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr, "silence_tokens": silence_tokens, "sample_batch_size": sample_batch_size}
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concated_audio, gen_audio = inference_one_sample(model, argparse.Namespace(
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**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)
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# save segments for comparison
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concated_audio, gen_audio = concated_audio[0].cpu(), gen_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
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output_dir = args.output_dir
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os.makedirs(output_dir, exist_ok=True)
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seg_save_fn_gen = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_gen_seed{seed}.wav"
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seg_save_fn_concat = f"{output_dir}/{os.path.basename(audio_fn)[:-4]}_concat_seed{seed}.wav"
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torchaudio.save(seg_save_fn_gen, gen_audio, codec_audio_sr)
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torchaudio.save(seg_save_fn_concat, concated_audio, codec_audio_sr)
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# you might get warnings like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored
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