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VoiceCraft
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update replicate demo

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chenxwh 2024-04-14 12:15:23 +00:00
parent e3fc926ca4
commit 0da8ee4b7a
2 changed files with 152 additions and 56 deletions
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2
cog.yaml
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@ -17,6 +17,8 @@ build:
- phonemizer==3.2.1
- datasets==2.16.0
- torchmetrics==0.11.1
- whisperx==3.1.1
- openai-whisper>=20231117
run:
- curl -O https://repo.anaconda.com/miniconda/Miniconda3-py310_23.3.1-0-Linux-x86_64.sh
- bash Miniconda3-py310_23.3.1-0-Linux-x86_64.sh -b -p /cog/miniconda

206
predict.py
View File

@ -2,16 +2,20 @@
# https://github.com/replicate/cog/blob/main/docs/python.md
import os
import stat
import time
import numpy as np
import warnings
import random
import getpass
import torch
import torchaudio
import shutil
import subprocess
import sys
import warnings
import torch
import numpy as np
import torchaudio
from whisper.model import Whisper, ModelDimensions
from whisper.tokenizer import get_tokenizer
from cog import BasePredictor, Input, Path, BaseModel
warnings.filterwarnings("ignore", category=UserWarning)
os.environ["USER"] = getpass.getuser()
@ -20,7 +24,6 @@ from data.tokenizer import (
AudioTokenizer,
TextTokenizer,
)
from cog import BasePredictor, Input, Path
from models import voicecraft
from inference_tts_scale import inference_one_sample
from edit_utils import get_span
@ -31,11 +34,38 @@ from inference_speech_editing_scale import (
ENV_NAME = "myenv"
MODEL_URL = "https://weights.replicate.delivery/default/VoiceCraft.tar"
MODEL_URL = "https://weights.replicate.delivery/default/pyp1/VoiceCraft.tar"
MODEL_CACHE = "model_cache"
class ModelOutput(BaseModel):
whisper_transcript_orig_audio: str
generated_audio: Path
class WhisperModel:
def __init__(self, model_cache, model_name="base.en", device="cuda"):
with open(f"{model_cache}/{model_name}.pt", "rb") as fp:
checkpoint = torch.load(fp, map_location="cpu")
dims = ModelDimensions(**checkpoint["dims"])
self.model = Whisper(dims)
self.model.load_state_dict(checkpoint["model_state_dict"])
self.model.to(device)
tokenizer = get_tokenizer(multilingual=False)
self.supress_tokens = [-1] + [
i
for i in range(tokenizer.eot)
if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
]
def transcribe(self, audio_path):
return self.model.transcribe(
audio_path, suppress_tokens=self.supress_tokens, word_timestamps=True
)["segments"]
def download_weights(url, dest):
start = time.time()
print("downloading url: ", url)
@ -49,56 +79,87 @@ class Predictor(BasePredictor):
"""Load the model into memory to make running multiple predictions efficient"""
self.device = "cuda"
voicecraft_name = "giga830M.pth" # or giga330M.pth
if not os.path.exists(MODEL_CACHE):
download_weights(MODEL_URL, MODEL_CACHE)
encodec_fn = f"{MODEL_CACHE}/encodec_4cb2048_giga.th"
ckpt_fn = f"{MODEL_CACHE}/{voicecraft_name}"
self.models, self.ckpt, self.phn2num = {}, {}, {}
for voicecraft_name in [
"giga830M.pth",
"giga330M.pth",
"gigaHalfLibri330M_TTSEnhanced_max16s.pth",
]:
ckpt_fn = f"{MODEL_CACHE}/{voicecraft_name}"
self.ckpt = torch.load(ckpt_fn, map_location="cpu")
self.model = voicecraft.VoiceCraft(self.ckpt["config"])
self.model.load_state_dict(self.ckpt["model"])
self.model.to(self.device)
self.model.eval()
self.ckpt[voicecraft_name] = torch.load(ckpt_fn, map_location="cpu")
self.models[voicecraft_name] = voicecraft.VoiceCraft(
self.ckpt[voicecraft_name]["config"]
)
self.models[voicecraft_name].load_state_dict(
self.ckpt[voicecraft_name]["model"]
)
self.models[voicecraft_name].to(self.device)
self.models[voicecraft_name].eval()
self.phn2num = self.ckpt["phn2num"]
self.phn2num[voicecraft_name] = self.ckpt[voicecraft_name]["phn2num"]
self.text_tokenizer = TextTokenizer(backend="espeak")
self.audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=self.device)
self.transcribe_models = {
k: WhisperModel(MODEL_CACHE, k, self.device)
for k in ["base.en", "small.en", "medium.en"]
}
def predict(
self,
task: str = Input(
description="Choose a task. For zero-shot text-to-speech, you also need to specify the cut_off_sec of the original audio to be used for zero-shot generation and the transcript until the cut_off_sec",
description="Choose a task",
choices=[
"speech_editing-substitution",
"speech_editing-insertion",
"speech_editing-deletion",
"zero-shot text-to-speech",
],
default="speech_editing-substitution",
default="zero-shot text-to-speech",
),
voicecraft_model: str = Input(
description="Choose a model",
choices=["giga830M.pth", "giga330M.pth", "giga330M_TTSEnhanced.pth"],
default="giga330M_TTSEnhanced.pth",
),
orig_audio: Path = Input(
description="Original audio file. WhisperX small.en model will be used for transcription"
),
orig_audio: Path = Input(description="Original audio file"),
orig_transcript: str = Input(
description="Transcript of the original audio file. You can use models such as https://replicate.com/openai/whisper and https://replicate.com/vaibhavs10/incredibly-fast-whisper to get the transcript (and modify it if it's not accurate)",
description="Optionally provide the transcript of the input audio. Leave it blank to use the whisper model below to generate the transcript. Inaccurate transcription may lead to error TTS or speech editing",
default="",
),
whisper_model: str = Input(
description="If orig_transcript is not provided above, choose a Whisper model. Inaccurate transcription may lead to error TTS or speech editing. You can modify the generated transcript and provide it directly to ",
choices=["base.en", "small.en", "medium.en"],
default="base.en",
),
target_transcript: str = Input(
description="Transcript of the target audio file",
),
cut_off_sec: float = Input(
description="Valid/Required for zero-shot text-to-speech task. The first seconds of the original audio that are used for zero-shot text-to-speech (TTS). 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec",
default=None,
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",
default=3.01,
),
orig_transcript_until_cutoff_time: str = Input(
description="Valid/Required for zero-shot text-to-speech task. Transcript of the original audio file until the cut_off_sec specified above. This process will be improved and made automatically later",
default=None,
kvcache: int = Input(
description="Set to 0 to use less VRAM, but with slower inference",
default=1,
),
left_margin: float = Input(
description="Margin to the left of the editing segment",
default=0.08,
),
right_margin: float = Input(
description="Margin to the right of the editing segment",
default=0.08,
),
temperature: float = Input(
description="Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic",
ge=0.01,
le=5,
description="Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic. Do not recommend to change",
default=1,
),
top_p: float = Input(
@ -109,28 +170,33 @@ class Predictor(BasePredictor):
),
stop_repetition: int = Input(
default=-1,
description=" -1 means do not adjust prob of silence tokens. if there are long silence or unnaturally strecthed words, increase sample_batch_size to 2, 3 or even 4",
description=" -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",
),
sampling_rate: int = Input(
description="Specify the sampling rate of the audio codec", default=16000
sample_batch_size: int = Input(
description="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",
default=4,
),
seed: int = Input(
description="Random seed. Leave blank to randomize the seed", default=None
),
) -> Path:
) -> ModelOutput:
"""Run a single prediction on the model"""
if task == "zero-shot text-to-speech":
assert (
orig_transcript_until_cutoff_time is not None
and cut_off_sec is not None
), "Please provide cut_off_sec and orig_transcript_until_cutoff_time for zero-shot text-to-speech task."
if seed is None:
seed = int.from_bytes(os.urandom(2), "big")
print(f"Using seed: {seed}")
seed_everything(seed)
segments = self.transcribe_models[whisper_model].transcribe(str(orig_audio))
state = get_transcribe_state(segments)
whisper_transcript = state["transcript"].strip()
if len(orig_transcript.strip()) == 0:
orig_transcript = whisper_transcript
print(f"The transcript from the Whisper model: {whisper_transcript}")
temp_folder = "exp_dir"
if os.path.exists(temp_folder):
shutil.rmtree(temp_folder)
@ -161,14 +227,13 @@ class Predictor(BasePredictor):
audio_dur = info.num_frames / info.sample_rate
# hyperparameters for inference
left_margin = 0.08
right_margin = 0.08
codec_audio_sr = 16000
codec_sr = 50
top_k = 0
silence_tokens = [1388, 1898, 131]
kvcache = 1 if task == "zero-shot text-to-speech" else 0
sample_batch_size = 4 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 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.
if voicecraft_model == "giga330M_TTSEnhanced.pth":
voicecraft_model = "gigaHalfLibri330M_TTSEnhanced_max16s.pth"
if task == "zero-shot text-to-speech":
assert (
@ -176,8 +241,11 @@ class Predictor(BasePredictor):
), 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)
idx = find_closest_cut_off_word(state["word_bounds"], cut_off_sec)
orig_transcript_until_cutoff_time = "".join(
[word_bound["word"] for word_bound in state["word_bounds"][:idx]]
)
else:
edit_type = task.split("-")[-1]
orig_span, new_span = get_span(
orig_transcript, target_transcript, edit_type
@ -212,18 +280,17 @@ class Predictor(BasePredictor):
"temperature": temperature,
"stop_repetition": stop_repetition,
"kvcache": kvcache,
"codec_audio_sr": sampling_rate,
"codec_audio_sr": codec_audio_sr,
"codec_sr": codec_sr,
"silence_tokens": silence_tokens,
}
if task == "zero-shot text-to-speech":
decode_config["sample_batch_size"] = sample_batch_size
concated_audio, gen_audio = inference_one_sample(
self.model,
self.ckpt["config"],
self.phn2num,
_, gen_audio = inference_one_sample(
self.models[voicecraft_model],
self.ckpt[voicecraft_model]["config"],
self.phn2num[voicecraft_model],
self.text_tokenizer,
self.audio_tokenizer,
audio_fn,
@ -234,12 +301,11 @@ class Predictor(BasePredictor):
decode_config,
prompt_end_frame,
)
else:
orig_audio, gen_audio = inference_one_sample_editing(
self.model,
self.ckpt["config"],
self.phn2num,
_, gen_audio = inference_one_sample_editing(
self.models[voicecraft_model],
self.ckpt[voicecraft_model]["config"],
self.phn2num[voicecraft_model],
self.text_tokenizer,
self.audio_tokenizer,
audio_fn,
@ -253,8 +319,10 @@ class Predictor(BasePredictor):
gen_audio = gen_audio[0].cpu()
out = "/tmp/out.wav"
torchaudio.save(out, gen_audio, sampling_rate)
return Path(out)
torchaudio.save(out, gen_audio, codec_audio_sr)
return ModelOutput(
generated_audio=Path(out), whisper_transcript_orig_audio=whisper_transcript
)
def seed_everything(seed):
@ -265,3 +333,29 @@ def seed_everything(seed):
torch.cuda.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def get_transcribe_state(segments):
words_info = [word_info for segment in segments for word_info in segment["words"]]
return {
"transcript": " ".join([segment["text"].strip() for segment in segments]),
"word_bounds": [
{"word": word["word"], "start": word["start"], "end": word["end"]}
for word in words_info
],
}
def find_closest_cut_off_word(word_bounds, cut_off_sec):
min_distance = float("inf")
for i, word_bound in enumerate(word_bounds):
distance = abs(word_bound["start"] - cut_off_sec)
if distance < min_distance:
min_distance = distance
if word_bound["end"] > cut_off_sec:
break
return i