mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
301 lines
9.5 KiB
Python
301 lines
9.5 KiB
Python
#!/usr/bin/env python3
|
|
# Copyright 2023 (authors: Feiteng Li)
|
|
# Copyright 2024 (authors: Yuekai Zhang)
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
This script is used to synthesize speech from text prompts and audio prompts.
|
|
Usage example:
|
|
python3 valle/infer.py --output-dir demos_epoch_${epoch}_avg_${avg} \
|
|
--checkpoint=${exp_dir}/epoch-${epoch}-avg-${avg}.pt \
|
|
--text-prompts "KNOT one point one five miles per hour." \
|
|
--audio-prompts ./prompts/8463_294825_000043_000000.wav \
|
|
--text "To get up and running quickly just follow the steps below."
|
|
|
|
top_p=1.0
|
|
python3 valle/infer.py --output-dir demos_epoch_${epoch}_avg_${avg}_top_p_${top_p} \
|
|
--top-k -1 --temperature 1.0 \
|
|
--text ./aishell3.txt \
|
|
--checkpoint ${exp_dir}/epoch-${epoch}-avg-${avg}.pt \
|
|
--text-extractor pypinyin_initials_finals --top-p ${top_p}
|
|
|
|
"""
|
|
import argparse
|
|
import logging
|
|
import os
|
|
from pathlib import Path
|
|
|
|
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
|
|
|
|
import torch
|
|
import torchaudio
|
|
from compute_neural_codec_and_prepare_text_tokens import (
|
|
AudioTokenizer,
|
|
TextTokenizer,
|
|
tokenize_text,
|
|
)
|
|
from encodec.utils import convert_audio
|
|
from k2 import symbol_table
|
|
from tokenizer import get_text_token_collater
|
|
from valle import VALLE
|
|
|
|
from icefall.utils import AttributeDict, str2bool
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument(
|
|
"--text-prompts",
|
|
type=str,
|
|
default="",
|
|
help="Text prompts which are separated by |.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--audio-prompts",
|
|
type=str,
|
|
default="",
|
|
help="Audio prompts which are separated by | and should be aligned with --text-prompts.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--text",
|
|
type=str,
|
|
default="",
|
|
help="prompt text\t prompt audio\ttarget text\ttarget audio",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--text-extractor",
|
|
type=str,
|
|
default="espeak",
|
|
help="espeak or pypinyin or pypinyin_initials_finals",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--checkpoint",
|
|
type=str,
|
|
default="exp/vallf_nano_full/checkpoint-100000.pt",
|
|
help="Path to the saved checkpoint.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--output-dir",
|
|
type=Path,
|
|
default=Path("infer/demo"),
|
|
help="Path to the tokenized files.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--top-k",
|
|
type=int,
|
|
default=-100,
|
|
help="Whether AR Decoder do top_k(if > 0) sampling.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--top-p",
|
|
type=float,
|
|
default=1.0,
|
|
help="Whether AR Decoder do top_p(if > 0) sampling.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--temperature",
|
|
type=float,
|
|
default=1.0,
|
|
help="The temperature of AR Decoder top_k sampling.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--continual",
|
|
type=str2bool,
|
|
default=False,
|
|
help="Do continual task.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--repetition-aware-sampling",
|
|
type=str2bool,
|
|
default=False,
|
|
help="Whether AR Decoder do valle-2 repetition-aware sampling. https://arxiv.org/pdf/2406.05370",
|
|
)
|
|
|
|
return parser.parse_args()
|
|
|
|
|
|
def load_model(checkpoint, device):
|
|
if not checkpoint:
|
|
return None
|
|
|
|
checkpoint = torch.load(checkpoint, map_location=device)
|
|
|
|
params = AttributeDict(checkpoint)
|
|
model = VALLE(
|
|
params.decoder_dim,
|
|
params.nhead,
|
|
params.num_decoder_layers,
|
|
norm_first=params.norm_first,
|
|
add_prenet=params.add_prenet,
|
|
prefix_mode=params.prefix_mode,
|
|
share_embedding=params.share_embedding,
|
|
nar_scale_factor=params.scale_factor,
|
|
prepend_bos=params.prepend_bos,
|
|
num_quantizers=params.num_quantizers,
|
|
)
|
|
|
|
missing_keys, unexpected_keys = model.load_state_dict(
|
|
checkpoint["model"], strict=True
|
|
)
|
|
assert not missing_keys
|
|
model.to(device)
|
|
model.eval()
|
|
|
|
return model, params.text_tokens
|
|
|
|
|
|
def tokenize_audio(tokenizer: AudioTokenizer, audio_path: str):
|
|
# Load and pre-process the audio waveform
|
|
wav, sr = torchaudio.load(audio_path)
|
|
wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels)
|
|
wav = wav.unsqueeze(0)
|
|
|
|
# Extract discrete codes from EnCodec
|
|
with torch.no_grad():
|
|
encoded_frames = tokenizer.encode(wav)
|
|
return encoded_frames
|
|
|
|
|
|
@torch.no_grad()
|
|
def main():
|
|
args = get_args()
|
|
text_tokenizer = TextTokenizer(backend=args.text_extractor)
|
|
device = torch.device("cpu")
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda", 0)
|
|
|
|
model, text_tokens = load_model(args.checkpoint, device)
|
|
|
|
text_collater = get_text_token_collater(text_tokens)
|
|
|
|
audio_tokenizer = AudioTokenizer()
|
|
|
|
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
|
|
|
|
text_prompts = " ".join(args.text_prompts.split("|"))
|
|
|
|
audio_prompts = []
|
|
if args.audio_prompts:
|
|
for n, audio_file in enumerate(args.audio_prompts.split("|")):
|
|
encoded_frames = tokenize_audio(audio_tokenizer, audio_file)
|
|
if False:
|
|
samples = audio_tokenizer.decode(encoded_frames)
|
|
torchaudio.save(f"{args.output_dir}/p{n}.wav", samples[0], 24000)
|
|
|
|
audio_prompts.append(encoded_frames[0][0])
|
|
|
|
assert len(args.text_prompts.split("|")) == len(audio_prompts)
|
|
audio_prompts = torch.concat(audio_prompts, dim=-1).transpose(2, 1)
|
|
audio_prompts = audio_prompts.to(device)
|
|
|
|
if os.path.isfile(args.text): # for demos
|
|
# https://github.com/lifeiteng/lifeiteng.github.com/blob/main/valle/prepare.py
|
|
with open(args.text) as f:
|
|
for line in f:
|
|
fields = line.strip().split(" ")
|
|
fields = [item for item in fields if item]
|
|
assert len(fields) == 4
|
|
prompt_text, prompt_audio, text, audio_path = fields
|
|
logging.info(f"synthesize text: {text}")
|
|
text_tokens, text_tokens_lens = text_collater(
|
|
[
|
|
tokenize_text(
|
|
text_tokenizer, text=f"{prompt_text} {text}".strip()
|
|
)
|
|
]
|
|
)
|
|
_, enroll_x_lens = text_collater(
|
|
[tokenize_text(text_tokenizer, text=f"{prompt_text}".strip())]
|
|
)
|
|
|
|
audio_prompts = tokenize_audio(audio_tokenizer, prompt_audio)
|
|
audio_prompts = audio_prompts[0][0].transpose(2, 1).to(device)
|
|
|
|
# synthesis
|
|
encoded_frames = model.inference(
|
|
text_tokens.to(device),
|
|
text_tokens_lens.to(device),
|
|
audio_prompts,
|
|
enroll_x_lens=enroll_x_lens,
|
|
top_k=args.top_k,
|
|
temperature=args.temperature,
|
|
top_p=args.top_p,
|
|
ras=args.repetition_aware_sampling,
|
|
)
|
|
|
|
samples = audio_tokenizer.decode(
|
|
[(encoded_frames.transpose(2, 1), None)]
|
|
)
|
|
# store
|
|
# save audio path into args.output_dir + audio_path
|
|
audio_path = f"{args.output_dir}/{audio_path}"
|
|
# mkdir -p
|
|
os.makedirs(os.path.dirname(audio_path), exist_ok=True)
|
|
torchaudio.save(audio_path, samples[0].cpu(), 24000)
|
|
return
|
|
|
|
for n, text in enumerate(args.text.split("|")):
|
|
logging.info(f"synthesize text: {text}")
|
|
text_tokens, text_tokens_lens = text_collater(
|
|
[tokenize_text(text_tokenizer, text=f"{text_prompts} {text}".strip())]
|
|
)
|
|
|
|
# synthesis
|
|
if args.continual:
|
|
assert text == ""
|
|
encoded_frames = model.continual(
|
|
text_tokens.to(device),
|
|
text_tokens_lens.to(device),
|
|
audio_prompts,
|
|
)
|
|
else:
|
|
enroll_x_lens = None
|
|
if text_prompts:
|
|
_, enroll_x_lens = text_collater(
|
|
[tokenize_text(text_tokenizer, text=f"{text_prompts}".strip())]
|
|
)
|
|
encoded_frames = model.inference(
|
|
text_tokens.to(device),
|
|
text_tokens_lens.to(device),
|
|
audio_prompts,
|
|
enroll_x_lens=enroll_x_lens,
|
|
top_k=args.top_k,
|
|
temperature=args.temperature,
|
|
top_p=args.top_p,
|
|
ras=args.repetition_aware_sampling,
|
|
)
|
|
|
|
if audio_prompts != []:
|
|
samples = audio_tokenizer.decode([(encoded_frames.transpose(2, 1), None)])
|
|
# store
|
|
torchaudio.save(f"{args.output_dir}/{n}.wav", samples[0].cpu(), 24000)
|
|
else: # Transformer
|
|
pass
|
|
|
|
|
|
if __name__ == "__main__":
|
|
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
|
logging.basicConfig(format=formatter, level=logging.INFO)
|
|
main()
|