You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/paddlespeech/t2s/exps/syn_utils.py

501 lines
18 KiB

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import math
import os
from pathlib import Path
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
import numpy as np
import onnxruntime as ort
import paddle
from paddle import inference
from paddle import jit
from paddle.static import InputSpec
from yacs.config import CfgNode
from paddlespeech.t2s.datasets.data_table import DataTable
from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.mix_frontend import MixFrontend
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.modules.normalizer import ZScore
from paddlespeech.utils.dynamic_import import dynamic_import
# remove [W:onnxruntime: xxx] from ort
ort.set_default_logger_severity(3)
model_alias = {
# acoustic model
"speedyspeech":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
"speedyspeech_inference":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
"tacotron2":
"paddlespeech.t2s.models.tacotron2:Tacotron2",
"tacotron2_inference":
"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
# voc
"pwgan":
"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
"pwgan_inference":
"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
"style_melgan":
"paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
"style_melgan_inference":
"paddlespeech.t2s.models.melgan:StyleMelGANInference",
"hifigan":
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
"hifigan_inference":
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
"wavernn":
"paddlespeech.t2s.models.wavernn:WaveRNN",
"wavernn_inference":
"paddlespeech.t2s.models.wavernn:WaveRNNInference",
"erniesat":
"paddlespeech.t2s.models.ernie_sat:ErnieSAT",
"erniesat_inference":
"paddlespeech.t2s.models.ernie_sat:ErnieSATInference",
}
def denorm(data, mean, std):
return data * std + mean
def norm(data, mean, std):
return (data - mean) / std
def get_chunks(data, block_size: int, pad_size: int):
data_len = data.shape[1]
chunks = []
n = math.ceil(data_len / block_size)
for i in range(n):
start = max(0, i * block_size - pad_size)
end = min((i + 1) * block_size + pad_size, data_len)
chunks.append(data[:, start:end, :])
return chunks
# input
def get_sentences(text_file: Optional[os.PathLike], lang: str='zh'):
# construct dataset for evaluation
sentences = []
with open(text_file, 'rt') as f:
for line in f:
items = line.strip().split()
utt_id = items[0]
if lang == 'zh':
sentence = "".join(items[1:])
elif lang == 'en':
sentence = " ".join(items[1:])
elif lang == 'mix':
sentence = " ".join(items[1:])
sentences.append((utt_id, sentence))
return sentences
def get_test_dataset(test_metadata: List[Dict[str, Any]],
am: str,
speaker_dict: Optional[os.PathLike]=None,
voice_cloning: bool=False):
# model: {model_name}_{dataset}
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
converters = {}
if am_name == 'fastspeech2':
fields = ["utt_id", "text"]
if am_dataset in {"aishell3", "vctk",
"mix"} and speaker_dict is not None:
print("multiple speaker fastspeech2!")
fields += ["spk_id"]
elif voice_cloning:
print("voice cloning!")
fields += ["spk_emb"]
else:
print("single speaker fastspeech2!")
elif am_name == 'speedyspeech':
fields = ["utt_id", "phones", "tones"]
elif am_name == 'tacotron2':
fields = ["utt_id", "text"]
if voice_cloning:
print("voice cloning!")
fields += ["spk_emb"]
elif am_name == 'erniesat':
fields = [
"utt_id", "text", "text_lengths", "speech", "speech_lengths",
"align_start", "align_end"
]
converters = {"speech": np.load}
else:
print("wrong am, please input right am!!!")
test_dataset = DataTable(
data=test_metadata, fields=fields, converters=converters)
return test_dataset
# frontend
def get_frontend(lang: str='zh',
phones_dict: Optional[os.PathLike]=None,
tones_dict: Optional[os.PathLike]=None):
if lang == 'zh':
frontend = Frontend(
phone_vocab_path=phones_dict, tone_vocab_path=tones_dict)
elif lang == 'en':
frontend = English(phone_vocab_path=phones_dict)
elif lang == 'mix':
frontend = MixFrontend(
phone_vocab_path=phones_dict, tone_vocab_path=tones_dict)
else:
print("wrong lang!")
return frontend
def run_frontend(frontend: object,
text: str,
merge_sentences: bool=False,
get_tone_ids: bool=False,
lang: str='zh',
to_tensor: bool=True):
outs = dict()
if lang == 'zh':
input_ids = frontend.get_input_ids(
text,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
to_tensor=to_tensor)
phone_ids = input_ids["phone_ids"]
if get_tone_ids:
tone_ids = input_ids["tone_ids"]
outs.update({'tone_ids': tone_ids})
elif lang == 'en':
input_ids = frontend.get_input_ids(
text, merge_sentences=merge_sentences, to_tensor=to_tensor)
phone_ids = input_ids["phone_ids"]
elif lang == 'mix':
input_ids = frontend.get_input_ids(
text, merge_sentences=merge_sentences, to_tensor=to_tensor)
phone_ids = input_ids["phone_ids"]
else:
print("lang should in {'zh', 'en', 'mix'}!")
outs.update({'phone_ids': phone_ids})
return outs
# dygraph
def get_am_inference(am: str='fastspeech2_csmsc',
am_config: CfgNode=None,
am_ckpt: Optional[os.PathLike]=None,
am_stat: Optional[os.PathLike]=None,
phones_dict: Optional[os.PathLike]=None,
tones_dict: Optional[os.PathLike]=None,
speaker_dict: Optional[os.PathLike]=None,
return_am: bool=False):
with open(phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
tone_size = None
if tones_dict is not None:
with open(tones_dict, "r") as f:
tone_id = [line.strip().split() for line in f.readlines()]
tone_size = len(tone_id)
spk_num = None
if speaker_dict is not None:
with open(speaker_dict, 'rt') as f:
spk_id = [line.strip().split() for line in f.readlines()]
spk_num = len(spk_id)
odim = am_config.n_mels
# model: {model_name}_{dataset}
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
am_class = dynamic_import(am_name, model_alias)
am_inference_class = dynamic_import(am_name + '_inference', model_alias)
if am_name == 'fastspeech2':
am = am_class(
idim=vocab_size, odim=odim, spk_num=spk_num, **am_config["model"])
elif am_name == 'speedyspeech':
am = am_class(
vocab_size=vocab_size,
tone_size=tone_size,
spk_num=spk_num,
**am_config["model"])
elif am_name == 'tacotron2':
am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
elif am_name == 'erniesat':
am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
else:
print("wrong am, please input right am!!!")
am.set_state_dict(paddle.load(am_ckpt)["main_params"])
am.eval()
am_mu, am_std = np.load(am_stat)
am_mu = paddle.to_tensor(am_mu)
am_std = paddle.to_tensor(am_std)
am_normalizer = ZScore(am_mu, am_std)
am_inference = am_inference_class(am_normalizer, am)
am_inference.eval()
if return_am:
return am_inference, am
else:
return am_inference
def get_voc_inference(
voc: str='pwgan_csmsc',
voc_config: Optional[os.PathLike]=None,
voc_ckpt: Optional[os.PathLike]=None,
voc_stat: Optional[os.PathLike]=None, ):
# model: {model_name}_{dataset}
voc_name = voc[:voc.rindex('_')]
voc_class = dynamic_import(voc_name, model_alias)
voc_inference_class = dynamic_import(voc_name + '_inference', model_alias)
if voc_name != 'wavernn':
voc = voc_class(**voc_config["generator_params"])
voc.set_state_dict(paddle.load(voc_ckpt)["generator_params"])
voc.remove_weight_norm()
voc.eval()
else:
voc = voc_class(**voc_config["model"])
voc.set_state_dict(paddle.load(voc_ckpt)["main_params"])
voc.eval()
voc_mu, voc_std = np.load(voc_stat)
voc_mu = paddle.to_tensor(voc_mu)
voc_std = paddle.to_tensor(voc_std)
voc_normalizer = ZScore(voc_mu, voc_std)
voc_inference = voc_inference_class(voc_normalizer, voc)
voc_inference.eval()
return voc_inference
# dygraph to static graph
def am_to_static(am_inference,
am: str='fastspeech2_csmsc',
inference_dir=Optional[os.PathLike],
speaker_dict: Optional[os.PathLike]=None):
# model: {model_name}_{dataset}
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
if am_name == 'fastspeech2':
if am_dataset in {"aishell3", "vctk",
"mix"} and speaker_dict is not None:
am_inference = jit.to_static(
am_inference,
input_spec=[
InputSpec([-1], dtype=paddle.int64),
InputSpec([1], dtype=paddle.int64),
], )
else:
am_inference = jit.to_static(
am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
elif am_name == 'speedyspeech':
if am_dataset in {"aishell3", "vctk",
"mix"} and speaker_dict is not None:
am_inference = jit.to_static(
am_inference,
input_spec=[
InputSpec([-1], dtype=paddle.int64), # text
InputSpec([-1], dtype=paddle.int64), # tone
InputSpec([1], dtype=paddle.int64), # spk_id
None # duration
])
else:
am_inference = jit.to_static(
am_inference,
input_spec=[
InputSpec([-1], dtype=paddle.int64),
InputSpec([-1], dtype=paddle.int64)
])
elif am_name == 'tacotron2':
am_inference = jit.to_static(
am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
paddle.jit.save(am_inference, os.path.join(inference_dir, am))
am_inference = paddle.jit.load(os.path.join(inference_dir, am))
return am_inference
def voc_to_static(voc_inference,
voc: str='pwgan_csmsc',
inference_dir=Optional[os.PathLike]):
voc_inference = jit.to_static(
voc_inference, input_spec=[
InputSpec([-1, 80], dtype=paddle.float32),
])
paddle.jit.save(voc_inference, os.path.join(inference_dir, voc))
voc_inference = paddle.jit.load(os.path.join(inference_dir, voc))
return voc_inference
# inference
def get_predictor(model_dir: Optional[os.PathLike]=None,
model_file: Optional[os.PathLike]=None,
params_file: Optional[os.PathLike]=None,
device: str='cpu'):
config = inference.Config(
str(Path(model_dir) / model_file), str(Path(model_dir) / params_file))
if device == "gpu":
config.enable_use_gpu(100, 0)
elif device == "cpu":
config.disable_gpu()
config.enable_memory_optim()
predictor = inference.create_predictor(config)
return predictor
def get_am_output(
input: str,
am_predictor: paddle.nn.Layer,
am: str,
frontend: object,
lang: str='zh',
merge_sentences: bool=True,
speaker_dict: Optional[os.PathLike]=None,
spk_id: int=0, ):
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
am_input_names = am_predictor.get_input_names()
get_spk_id = False
get_tone_ids = False
if am_name == 'speedyspeech':
get_tone_ids = True
if am_dataset in {"aishell3", "vctk", "mix"} and speaker_dict:
get_spk_id = True
spk_id = np.array([spk_id])
frontend_dict = run_frontend(
frontend=frontend,
text=input,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang)
if get_tone_ids:
tone_ids = frontend_dict['tone_ids']
tones = tone_ids[0].numpy()
tones_handle = am_predictor.get_input_handle(am_input_names[1])
tones_handle.reshape(tones.shape)
tones_handle.copy_from_cpu(tones)
if get_spk_id:
spk_id_handle = am_predictor.get_input_handle(am_input_names[1])
spk_id_handle.reshape(spk_id.shape)
spk_id_handle.copy_from_cpu(spk_id)
phone_ids = frontend_dict['phone_ids']
phones = phone_ids[0].numpy()
phones_handle = am_predictor.get_input_handle(am_input_names[0])
phones_handle.reshape(phones.shape)
phones_handle.copy_from_cpu(phones)
am_predictor.run()
am_output_names = am_predictor.get_output_names()
am_output_handle = am_predictor.get_output_handle(am_output_names[0])
am_output_data = am_output_handle.copy_to_cpu()
return am_output_data
def get_voc_output(voc_predictor, input):
voc_input_names = voc_predictor.get_input_names()
mel_handle = voc_predictor.get_input_handle(voc_input_names[0])
mel_handle.reshape(input.shape)
mel_handle.copy_from_cpu(input)
voc_predictor.run()
voc_output_names = voc_predictor.get_output_names()
voc_output_handle = voc_predictor.get_output_handle(voc_output_names[0])
wav = voc_output_handle.copy_to_cpu()
return wav
def get_am_sublayer_output(am_sublayer_predictor, input):
am_sublayer_input_names = am_sublayer_predictor.get_input_names()
input_handle = am_sublayer_predictor.get_input_handle(
am_sublayer_input_names[0])
input_handle.reshape(input.shape)
input_handle.copy_from_cpu(input)
am_sublayer_predictor.run()
am_sublayer_names = am_sublayer_predictor.get_output_names()
am_sublayer_handle = am_sublayer_predictor.get_output_handle(
am_sublayer_names[0])
am_sublayer_output = am_sublayer_handle.copy_to_cpu()
return am_sublayer_output
def get_streaming_am_output(input: str,
am_encoder_infer_predictor,
am_decoder_predictor,
am_postnet_predictor,
frontend,
lang: str='zh',
merge_sentences: bool=True):
get_tone_ids = False
frontend_dict = run_frontend(
frontend=frontend,
text=input,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang)
phone_ids = frontend_dict['phone_ids']
phones = phone_ids[0].numpy()
am_encoder_infer_output = get_am_sublayer_output(
am_encoder_infer_predictor, input=phones)
am_decoder_output = get_am_sublayer_output(
am_decoder_predictor, input=am_encoder_infer_output)
am_postnet_output = get_am_sublayer_output(
am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1)))
am_output_data = am_decoder_output + np.transpose(am_postnet_output,
(0, 2, 1))
normalized_mel = am_output_data[0]
return normalized_mel
# onnx
def get_sess(model_path: Optional[os.PathLike],
device: str='cpu',
cpu_threads: int=1,
use_trt: bool=False):
sess_options = ort.SessionOptions()
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
if 'gpu' in device.lower():
device_id = int(device.split(':')[1]) if len(
device.split(':')) == 2 else 0
# fastspeech2/mb_melgan can't use trt now!
if use_trt:
provider_name = 'TensorrtExecutionProvider'
else:
provider_name = 'CUDAExecutionProvider'
providers = [(provider_name, {'device_id': device_id})]
elif device.lower() == 'cpu':
providers = ['CPUExecutionProvider']
sess_options.intra_op_num_threads = cpu_threads
sess = ort.InferenceSession(
model_path, providers=providers, sess_options=sess_options)
return sess