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.
501 lines
18 KiB
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
|