390 lines
14 KiB
Python
390 lines
14 KiB
Python
# Prediction interface for Cog ⚙️
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# https://github.com/replicate/cog/blob/main/docs/python.md
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import os
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import time
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import random
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import getpass
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import shutil
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import subprocess
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import torch
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import numpy as np
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import torchaudio
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from cog import BasePredictor, Input, Path, BaseModel
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os.environ["USER"] = getpass.getuser()
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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 models import voicecraft
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from inference_tts_scale import inference_one_sample
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from edit_utils import get_span
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from inference_speech_editing_scale import (
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inference_one_sample as inference_one_sample_editing,
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)
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MODEL_URL = "https://weights.replicate.delivery/default/pyp1/VoiceCraft-models.tar" # all the models are cached and uploaded to replicate.delivery for faster booting
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MODEL_CACHE = "model_cache"
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class ModelOutput(BaseModel):
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whisper_transcript_orig_audio: str
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generated_audio: Path
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class WhisperxAlignModel:
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def __init__(self):
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from whisperx import load_align_model
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self.model, self.metadata = load_align_model(
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language_code="en", device="cuda:0"
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)
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def align(self, segments, audio_path):
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from whisperx import align, load_audio
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audio = load_audio(audio_path)
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return align(
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segments,
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self.model,
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self.metadata,
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audio,
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device="cuda:0",
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return_char_alignments=False,
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)["segments"]
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class WhisperxModel:
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def __init__(self, model_name, align_model: WhisperxAlignModel, device="cuda"):
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from whisperx import load_model
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# the model weights are cached from Systran/faster-whisper-base.en etc
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self.model = load_model(
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model_name,
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device,
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asr_options={
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"suppress_numerals": True,
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"max_new_tokens": None,
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"clip_timestamps": None,
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"hallucination_silence_threshold": None,
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},
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)
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self.align_model = align_model
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def transcribe(self, audio_path):
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segments = self.model.transcribe(audio_path, language="en", batch_size=8)[
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"segments"
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]
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return self.align_model.align(segments, audio_path)
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def download_weights(url, dest):
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start = time.time()
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print("downloading url: ", url)
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print("downloading to: ", dest)
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subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
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print("downloading took: ", time.time() - start)
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class Predictor(BasePredictor):
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def setup(self):
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"""Load the model into memory to make running multiple predictions efficient"""
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self.device = "cuda"
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if not os.path.exists(MODEL_CACHE):
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download_weights(MODEL_URL, MODEL_CACHE)
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encodec_fn = f"{MODEL_CACHE}/encodec_4cb2048_giga.th"
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self.models, self.ckpt, self.phn2num = {}, {}, {}
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for voicecraft_name in [
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"giga830M.pth",
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"giga330M.pth",
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"gigaHalfLibri330M_TTSEnhanced_max16s.pth",
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]:
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ckpt_fn = f"{MODEL_CACHE}/{voicecraft_name}"
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self.ckpt[voicecraft_name] = torch.load(ckpt_fn, map_location="cpu")
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self.models[voicecraft_name] = voicecraft.VoiceCraft(
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self.ckpt[voicecraft_name]["config"]
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)
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self.models[voicecraft_name].load_state_dict(
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self.ckpt[voicecraft_name]["model"]
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)
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self.models[voicecraft_name].to(self.device)
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self.models[voicecraft_name].eval()
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self.phn2num[voicecraft_name] = self.ckpt[voicecraft_name]["phn2num"]
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self.text_tokenizer = TextTokenizer(backend="espeak")
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self.audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=self.device)
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align_model = WhisperxAlignModel()
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self.transcribe_models = {
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k: WhisperxModel(f"{MODEL_CACHE}/whisperx_{k.split('.')[0]}", align_model)
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for k in ["base.en", "small.en", "medium.en"]
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}
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def predict(
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self,
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task: str = Input(
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description="Choose a task",
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choices=[
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"speech_editing-substitution",
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"speech_editing-insertion",
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"speech_editing-deletion",
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"zero-shot text-to-speech",
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],
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default="zero-shot text-to-speech",
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),
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voicecraft_model: str = Input(
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description="Choose a model",
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choices=["giga830M.pth", "giga330M.pth", "giga330M_TTSEnhanced.pth"],
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default="giga330M_TTSEnhanced.pth",
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),
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orig_audio: Path = Input(description="Original audio file"),
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orig_transcript: str = Input(
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description="Optionally provide the transcript of the input audio. Leave it blank to use the WhisperX model below to generate the transcript. Inaccurate transcription may lead to error TTS or speech editing",
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default="",
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),
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whisperx_model: str = Input(
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description="If orig_transcript is not provided above, choose a WhisperX model for generating the transcript. Inaccurate transcription may lead to error TTS or speech editing. You can modify the generated transcript and provide it directly to orig_transcript above",
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choices=[
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"base.en",
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"small.en",
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"medium.en",
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],
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default="base.en",
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),
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target_transcript: str = Input(
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description="Transcript of the target audio file",
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),
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cut_off_sec: float = Input(
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description="Only used for for zero-shot text-to-speech task. The first seconds of the original audio that are used for zero-shot text-to-speech. 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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default=3.01,
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),
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kvcache: int = Input(
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description="Set to 0 to use less VRAM, but with slower inference",
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choices=[0, 1],
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default=1,
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),
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left_margin: float = Input(
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description="Margin to the left of the editing segment",
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default=0.08,
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),
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right_margin: float = Input(
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description="Margin to the right of the editing segment",
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default=0.08,
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),
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temperature: float = Input(
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description="Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic. Do not recommend to change",
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default=1,
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),
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top_p: float = Input(
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description="Default value for TTS is 0.9, and 0.8 for speech editing",
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default=0.9,
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),
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stop_repetition: int = Input(
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default=3,
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description="Default value for TTS is 3, and -1 for speech editing. -1 means do not adjust prob of silence tokens. if there are long silence or unnaturally stretched words, increase sample_batch_size to 2, 3 or even 4",
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),
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sample_batch_size: int = Input(
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description="Default value for TTS is 4, and 1 for speech editing. The higher the number, the faster the output will be. Under the hood, the model will generate this many samples and choose the shortest one",
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default=4,
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),
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seed: int = Input(
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description="Random seed. Leave blank to randomize the seed", default=None
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),
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) -> ModelOutput:
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"""Run a single prediction on the model"""
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if seed is None:
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seed = int.from_bytes(os.urandom(2), "big")
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print(f"Using seed: {seed}")
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seed_everything(seed)
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segments = self.transcribe_models[whisperx_model].transcribe(
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str(orig_audio)
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)
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state = get_transcribe_state(segments)
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whisper_transcript = state["transcript"].strip()
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if len(orig_transcript.strip()) == 0:
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orig_transcript = whisper_transcript
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print(f"The transcript from the Whisper model: {whisper_transcript}")
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temp_folder = "exp_dir"
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if os.path.exists(temp_folder):
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shutil.rmtree(temp_folder)
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os.makedirs(temp_folder)
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filename = "orig_audio"
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audio_fn = str(orig_audio)
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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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# hyperparameters for inference
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codec_audio_sr = 16000
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codec_sr = 50
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top_k = 0
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silence_tokens = [1388, 1898, 131]
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if voicecraft_model == "giga330M_TTSEnhanced.pth":
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voicecraft_model = "gigaHalfLibri330M_TTSEnhanced_max16s.pth"
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if task == "zero-shot text-to-speech":
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assert (
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cut_off_sec < audio_dur
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), 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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idx = find_closest_cut_off_word(state["word_bounds"], cut_off_sec)
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orig_transcript_until_cutoff_time = " ".join(
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[word_bound["word"] for word_bound in state["word_bounds"][: idx + 1]]
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)
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else:
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edit_type = task.split("-")[-1]
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orig_span, new_span = get_span(
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orig_transcript, target_transcript, edit_type
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)
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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_from_word_bounds(
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state["word_bounds"], orig_span_save, edit_type
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)
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# span in codec frames
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morphed_span = (
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max(start - left_margin, 1 / codec_sr),
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min(end + right_margin, audio_dur),
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) # in seconds
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mask_interval = [
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[round(morphed_span[0] * codec_sr), round(morphed_span[1] * codec_sr)]
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]
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mask_interval = torch.LongTensor(mask_interval) # [M,2], M==1 for now
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decode_config = {
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"top_k": top_k,
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"top_p": top_p,
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"temperature": temperature,
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"stop_repetition": stop_repetition,
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"kvcache": kvcache,
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"codec_audio_sr": codec_audio_sr,
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"codec_sr": codec_sr,
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"silence_tokens": silence_tokens,
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}
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if task == "zero-shot text-to-speech":
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decode_config["sample_batch_size"] = sample_batch_size
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_, gen_audio = inference_one_sample(
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self.models[voicecraft_model],
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self.ckpt[voicecraft_model]["config"],
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self.phn2num[voicecraft_model],
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self.text_tokenizer,
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self.audio_tokenizer,
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audio_fn,
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orig_transcript_until_cutoff_time.strip()
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+ " "
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+ target_transcript.strip(),
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self.device,
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decode_config,
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prompt_end_frame,
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)
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else:
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_, gen_audio = inference_one_sample_editing(
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self.models[voicecraft_model],
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self.ckpt[voicecraft_model]["config"],
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self.phn2num[voicecraft_model],
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self.text_tokenizer,
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self.audio_tokenizer,
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audio_fn,
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target_transcript,
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mask_interval,
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self.device,
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decode_config,
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)
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# save segments for comparison
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gen_audio = gen_audio[0].cpu()
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out = "/tmp/out.wav"
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torchaudio.save(out, gen_audio, codec_audio_sr)
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return ModelOutput(
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generated_audio=Path(out), whisper_transcript_orig_audio=whisper_transcript
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)
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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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def get_transcribe_state(segments):
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words_info = [word_info for segment in segments for word_info in segment["words"]]
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return {
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"transcript": " ".join([segment["text"].strip() for segment in segments]),
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"word_bounds": [
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{"word": word["word"], "start": word["start"], "end": word["end"]}
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for word in words_info
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],
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}
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def find_closest_cut_off_word(word_bounds, cut_off_sec):
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min_distance = float("inf")
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for i, word_bound in enumerate(word_bounds):
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distance = abs(word_bound["start"] - cut_off_sec)
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if distance < min_distance:
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min_distance = distance
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if word_bound["end"] > cut_off_sec:
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break
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return i
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def get_mask_interval_from_word_bounds(word_bounds, word_span_ind, editType):
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tmp = word_span_ind.split(",")
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s, e = int(tmp[0]), int(tmp[-1])
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start = None
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for j, item in enumerate(word_bounds):
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if j == s:
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if editType == "insertion":
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start = float(item["end"])
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else:
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start = float(item["start"])
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if j == e:
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if editType == "insertion":
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end = float(item["start"])
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else:
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end = float(item["end"])
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assert start is not None
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break
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return (start, end)
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