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@ -14,6 +14,10 @@
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import math
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import os
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from pathlib import Path
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from typing import Any
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from typing import Dict
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from typing import List
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from typing import Optional
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import numpy as np
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import onnxruntime as ort
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@ -21,6 +25,7 @@ import paddle
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from paddle import inference
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from paddle import jit
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from paddle.static import InputSpec
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from yacs.config import CfgNode
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.t2s.datasets.data_table import DataTable
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@ -70,7 +75,7 @@ def denorm(data, mean, std):
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return data * std + mean
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def get_chunks(data, chunk_size, pad_size):
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def get_chunks(data, chunk_size: int, pad_size: int):
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data_len = data.shape[1]
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chunks = []
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n = math.ceil(data_len / chunk_size)
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@ -82,28 +87,34 @@ def get_chunks(data, chunk_size, pad_size):
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# input
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def get_sentences(args):
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def get_sentences(text_file: Optional[os.PathLike], lang: str='zh'):
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# construct dataset for evaluation
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sentences = []
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with open(args.text, 'rt') as f:
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with open(text_file, 'rt') as f:
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for line in f:
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items = line.strip().split()
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utt_id = items[0]
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if 'lang' in args and args.lang == 'zh':
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if lang == 'zh':
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sentence = "".join(items[1:])
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elif 'lang' in args and args.lang == 'en':
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elif lang == 'en':
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sentence = " ".join(items[1:])
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sentences.append((utt_id, sentence))
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return sentences
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def get_test_dataset(args, test_metadata, am_name, am_dataset):
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def get_test_dataset(test_metadata: List[Dict[str, Any]],
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am: str,
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speaker_dict: Optional[os.PathLike]=None,
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voice_cloning: bool=False):
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# model: {model_name}_{dataset}
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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if am_name == 'fastspeech2':
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fields = ["utt_id", "text"]
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if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
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if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None:
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print("multiple speaker fastspeech2!")
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fields += ["spk_id"]
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elif 'voice_cloning' in args and args.voice_cloning:
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elif voice_cloning:
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print("voice cloning!")
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fields += ["spk_emb"]
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else:
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@ -112,7 +123,7 @@ def get_test_dataset(args, test_metadata, am_name, am_dataset):
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fields = ["utt_id", "phones", "tones"]
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elif am_name == 'tacotron2':
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fields = ["utt_id", "text"]
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if 'voice_cloning' in args and args.voice_cloning:
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if voice_cloning:
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print("voice cloning!")
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fields += ["spk_emb"]
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@ -121,12 +132,14 @@ def get_test_dataset(args, test_metadata, am_name, am_dataset):
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# frontend
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def get_frontend(args):
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if 'lang' in args and args.lang == 'zh':
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def get_frontend(lang: str='zh',
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phones_dict: Optional[os.PathLike]=None,
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tones_dict: Optional[os.PathLike]=None):
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if lang == 'zh':
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frontend = Frontend(
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phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
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elif 'lang' in args and args.lang == 'en':
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frontend = English(phone_vocab_path=args.phones_dict)
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phone_vocab_path=phones_dict, tone_vocab_path=tones_dict)
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elif lang == 'en':
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frontend = English(phone_vocab_path=phones_dict)
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else:
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print("wrong lang!")
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print("frontend done!")
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@ -134,30 +147,37 @@ def get_frontend(args):
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# dygraph
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def get_am_inference(args, am_config):
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with open(args.phones_dict, "r") as f:
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def get_am_inference(
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am: str='fastspeech2_csmsc',
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am_config: CfgNode=None,
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am_ckpt: Optional[os.PathLike]=None,
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am_stat: Optional[os.PathLike]=None,
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phones_dict: Optional[os.PathLike]=None,
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tones_dict: Optional[os.PathLike]=None,
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speaker_dict: Optional[os.PathLike]=None, ):
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with open(phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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tone_size = None
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if 'tones_dict' in args and args.tones_dict:
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with open(args.tones_dict, "r") as f:
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if tones_dict is not None:
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with open(tones_dict, "r") as f:
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tone_id = [line.strip().split() for line in f.readlines()]
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tone_size = len(tone_id)
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print("tone_size:", tone_size)
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spk_num = None
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if 'speaker_dict' in args and args.speaker_dict:
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with open(args.speaker_dict, 'rt') as f:
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if speaker_dict is not None:
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with open(speaker_dict, 'rt') as f:
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spk_id = [line.strip().split() for line in f.readlines()]
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spk_num = len(spk_id)
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print("spk_num:", spk_num)
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odim = am_config.n_mels
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# model: {model_name}_{dataset}
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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am_class = dynamic_import(am_name, model_alias)
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am_inference_class = dynamic_import(am_name + '_inference', model_alias)
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@ -174,34 +194,38 @@ def get_am_inference(args, am_config):
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elif am_name == 'tacotron2':
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am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
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am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
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am.set_state_dict(paddle.load(am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(args.am_stat)
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am_mu, am_std = np.load(am_stat)
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am_mu = paddle.to_tensor(am_mu)
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am_std = paddle.to_tensor(am_std)
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am_normalizer = ZScore(am_mu, am_std)
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am_inference = am_inference_class(am_normalizer, am)
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am_inference.eval()
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print("acoustic model done!")
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return am_inference, am_name, am_dataset
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return am_inference
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def get_voc_inference(args, voc_config):
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def get_voc_inference(
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voc: str='pwgan_csmsc',
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voc_config: Optional[os.PathLike]=None,
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voc_ckpt: Optional[os.PathLike]=None,
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voc_stat: Optional[os.PathLike]=None, ):
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# model: {model_name}_{dataset}
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voc_name = args.voc[:args.voc.rindex('_')]
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voc_name = voc[:voc.rindex('_')]
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voc_class = dynamic_import(voc_name, model_alias)
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voc_inference_class = dynamic_import(voc_name + '_inference', model_alias)
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if voc_name != 'wavernn':
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voc = voc_class(**voc_config["generator_params"])
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voc.set_state_dict(paddle.load(args.voc_ckpt)["generator_params"])
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voc.set_state_dict(paddle.load(voc_ckpt)["generator_params"])
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voc.remove_weight_norm()
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voc.eval()
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else:
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voc = voc_class(**voc_config["model"])
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voc.set_state_dict(paddle.load(args.voc_ckpt)["main_params"])
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voc.set_state_dict(paddle.load(voc_ckpt)["main_params"])
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voc.eval()
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voc_mu, voc_std = np.load(args.voc_stat)
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voc_mu, voc_std = np.load(voc_stat)
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voc_mu = paddle.to_tensor(voc_mu)
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voc_std = paddle.to_tensor(voc_std)
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voc_normalizer = ZScore(voc_mu, voc_std)
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@ -211,10 +235,16 @@ def get_voc_inference(args, voc_config):
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return voc_inference
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# to static
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def am_to_static(args, am_inference, am_name, am_dataset):
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# dygraph to static graph
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def am_to_static(am_inference,
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am: str='fastspeech2_csmsc',
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inference_dir=Optional[os.PathLike],
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speaker_dict: Optional[os.PathLike]=None):
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# model: {model_name}_{dataset}
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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if am_name == 'fastspeech2':
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if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
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if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None:
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am_inference = jit.to_static(
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am_inference,
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input_spec=[
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@ -226,7 +256,7 @@ def am_to_static(args, am_inference, am_name, am_dataset):
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am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
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elif am_name == 'speedyspeech':
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if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
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if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None:
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am_inference = jit.to_static(
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am_inference,
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input_spec=[
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@ -247,56 +277,64 @@ def am_to_static(args, am_inference, am_name, am_dataset):
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am_inference = jit.to_static(
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am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
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paddle.jit.save(am_inference, os.path.join(args.inference_dir, args.am))
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am_inference = paddle.jit.load(os.path.join(args.inference_dir, args.am))
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paddle.jit.save(am_inference, os.path.join(inference_dir, am))
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am_inference = paddle.jit.load(os.path.join(inference_dir, am))
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return am_inference
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def voc_to_static(args, voc_inference):
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def voc_to_static(voc_inference,
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voc: str='pwgan_csmsc',
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inference_dir=Optional[os.PathLike]):
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voc_inference = jit.to_static(
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voc_inference, input_spec=[
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InputSpec([-1, 80], dtype=paddle.float32),
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])
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paddle.jit.save(voc_inference, os.path.join(args.inference_dir, args.voc))
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voc_inference = paddle.jit.load(os.path.join(args.inference_dir, args.voc))
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paddle.jit.save(voc_inference, os.path.join(inference_dir, voc))
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voc_inference = paddle.jit.load(os.path.join(inference_dir, voc))
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return voc_inference
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# inference
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def get_predictor(args, filed='am'):
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full_name = ''
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if filed == 'am':
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full_name = args.am
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elif filed == 'voc':
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full_name = args.voc
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def get_predictor(model_dir: Optional[os.PathLike]=None,
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model_file: Optional[os.PathLike]=None,
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params_file: Optional[os.PathLike]=None,
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device: str='cpu'):
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config = inference.Config(
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str(Path(args.inference_dir) / (full_name + ".pdmodel")),
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str(Path(args.inference_dir) / (full_name + ".pdiparams")))
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if args.device == "gpu":
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str(Path(model_dir) / model_file), str(Path(model_dir) / params_file))
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if device == "gpu":
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config.enable_use_gpu(100, 0)
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elif args.device == "cpu":
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elif device == "cpu":
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config.disable_gpu()
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config.enable_memory_optim()
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predictor = inference.create_predictor(config)
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return predictor
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def get_am_output(args, am_predictor, frontend, merge_sentences, input):
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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def get_am_output(
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input: str,
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am_predictor,
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am,
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frontend,
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lang: str='zh',
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merge_sentences: bool=True,
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speaker_dict: Optional[os.PathLike]=None,
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spk_id: int=0, ):
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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am_input_names = am_predictor.get_input_names()
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get_tone_ids = False
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get_spk_id = False
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if am_name == 'speedyspeech':
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get_tone_ids = True
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if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
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if am_dataset in {"aishell3", "vctk"} and speaker_dict:
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get_spk_id = True
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spk_id = np.array([args.spk_id])
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if args.lang == 'zh':
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spk_id = np.array([spk_id])
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if lang == 'zh':
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input_ids = frontend.get_input_ids(
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input, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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elif args.lang == 'en':
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elif lang == 'en':
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input_ids = frontend.get_input_ids(
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input, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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@ -338,50 +376,6 @@ def get_voc_output(voc_predictor, input):
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return wav
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# streaming am
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def get_streaming_am_predictor(args):
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full_name = args.am
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am_encoder_infer_config = inference.Config(
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str(
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Path(args.inference_dir) /
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(full_name + "_am_encoder_infer" + ".pdmodel")),
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str(
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Path(args.inference_dir) /
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(full_name + "_am_encoder_infer" + ".pdiparams")))
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am_decoder_config = inference.Config(
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str(
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Path(args.inference_dir) /
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(full_name + "_am_decoder" + ".pdmodel")),
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str(
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Path(args.inference_dir) /
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(full_name + "_am_decoder" + ".pdiparams")))
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am_postnet_config = inference.Config(
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str(
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Path(args.inference_dir) /
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(full_name + "_am_postnet" + ".pdmodel")),
|
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str(
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Path(args.inference_dir) /
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(full_name + "_am_postnet" + ".pdiparams")))
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if args.device == "gpu":
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am_encoder_infer_config.enable_use_gpu(100, 0)
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am_decoder_config.enable_use_gpu(100, 0)
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am_postnet_config.enable_use_gpu(100, 0)
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elif args.device == "cpu":
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|
am_encoder_infer_config.disable_gpu()
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|
am_decoder_config.disable_gpu()
|
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|
am_postnet_config.disable_gpu()
|
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|
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|
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|
am_encoder_infer_config.enable_memory_optim()
|
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|
|
am_decoder_config.enable_memory_optim()
|
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|
|
|
am_postnet_config.enable_memory_optim()
|
|
|
|
|
|
|
|
|
|
am_encoder_infer_predictor = inference.create_predictor(
|
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|
|
|
am_encoder_infer_config)
|
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|
|
am_decoder_predictor = inference.create_predictor(am_decoder_config)
|
|
|
|
|
am_postnet_predictor = inference.create_predictor(am_postnet_config)
|
|
|
|
|
return am_encoder_infer_predictor, am_decoder_predictor, am_postnet_predictor
|
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|
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(
|
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|
|
|
@ -397,11 +391,15 @@ def get_am_sublayer_output(am_sublayer_predictor, input):
|
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|
|
return am_sublayer_output
|
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|
|
|
|
|
|
|
|
|
|
|
|
def get_streaming_am_output(args, am_encoder_infer_predictor,
|
|
|
|
|
am_decoder_predictor, am_postnet_predictor,
|
|
|
|
|
frontend, merge_sentences, input):
|
|
|
|
|
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
|
|
|
|
|
if args.lang == 'zh':
|
|
|
|
|
if lang == 'zh':
|
|
|
|
|
input_ids = frontend.get_input_ids(
|
|
|
|
|
input, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
|
|
|
|
|
phone_ids = input_ids["phone_ids"]
|
|
|
|
|
@ -423,58 +421,27 @@ def get_streaming_am_output(args, am_encoder_infer_predictor,
|
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|
|
|
return normalized_mel
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_sess(args, filed='am'):
|
|
|
|
|
full_name = ''
|
|
|
|
|
if filed == 'am':
|
|
|
|
|
full_name = args.am
|
|
|
|
|
elif filed == 'voc':
|
|
|
|
|
full_name = args.voc
|
|
|
|
|
model_dir = str(Path(args.inference_dir) / (full_name + ".onnx"))
|
|
|
|
|
# onnx
|
|
|
|
|
def get_sess(model_dir: Optional[os.PathLike]=None,
|
|
|
|
|
model_file: Optional[os.PathLike]=None,
|
|
|
|
|
device: str='cpu',
|
|
|
|
|
cpu_threads: int=1,
|
|
|
|
|
use_trt: bool=False):
|
|
|
|
|
|
|
|
|
|
model_dir = str(Path(model_dir) / model_file)
|
|
|
|
|
sess_options = ort.SessionOptions()
|
|
|
|
|
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
|
|
|
sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
|
|
|
|
|
|
|
|
|
|
if args.device == "gpu":
|
|
|
|
|
if device == "gpu":
|
|
|
|
|
# fastspeech2/mb_melgan can't use trt now!
|
|
|
|
|
if args.use_trt:
|
|
|
|
|
if use_trt:
|
|
|
|
|
providers = ['TensorrtExecutionProvider']
|
|
|
|
|
else:
|
|
|
|
|
providers = ['CUDAExecutionProvider']
|
|
|
|
|
elif args.device == "cpu":
|
|
|
|
|
elif device == "cpu":
|
|
|
|
|
providers = ['CPUExecutionProvider']
|
|
|
|
|
sess_options.intra_op_num_threads = args.cpu_threads
|
|
|
|
|
sess_options.intra_op_num_threads = cpu_threads
|
|
|
|
|
sess = ort.InferenceSession(
|
|
|
|
|
model_dir, providers=providers, sess_options=sess_options)
|
|
|
|
|
return sess
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# streaming am
|
|
|
|
|
def get_streaming_am_sess(args):
|
|
|
|
|
full_name = args.am
|
|
|
|
|
am_encoder_infer_model_dir = str(
|
|
|
|
|
Path(args.inference_dir) / (full_name + "_am_encoder_infer" + ".onnx"))
|
|
|
|
|
am_decoder_model_dir = str(
|
|
|
|
|
Path(args.inference_dir) / (full_name + "_am_decoder" + ".onnx"))
|
|
|
|
|
am_postnet_model_dir = str(
|
|
|
|
|
Path(args.inference_dir) / (full_name + "_am_postnet" + ".onnx"))
|
|
|
|
|
sess_options = ort.SessionOptions()
|
|
|
|
|
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
|
|
|
sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
|
|
|
|
|
if args.device == "gpu":
|
|
|
|
|
# fastspeech2/mb_melgan can't use trt now!
|
|
|
|
|
if args.use_trt:
|
|
|
|
|
providers = ['TensorrtExecutionProvider']
|
|
|
|
|
else:
|
|
|
|
|
providers = ['CUDAExecutionProvider']
|
|
|
|
|
elif args.device == "cpu":
|
|
|
|
|
providers = ['CPUExecutionProvider']
|
|
|
|
|
sess_options.intra_op_num_threads = args.cpu_threads
|
|
|
|
|
am_encoder_infer_sess = ort.InferenceSession(
|
|
|
|
|
am_encoder_infer_model_dir,
|
|
|
|
|
providers=providers,
|
|
|
|
|
sess_options=sess_options)
|
|
|
|
|
am_decoder_sess = ort.InferenceSession(
|
|
|
|
|
am_decoder_model_dir, providers=providers, sess_options=sess_options)
|
|
|
|
|
am_postnet_sess = ort.InferenceSession(
|
|
|
|
|
am_postnet_model_dir, providers=providers, sess_options=sess_options)
|
|
|
|
|
return am_encoder_infer_sess, am_decoder_sess, am_postnet_sess
|
|
|
|
|
|
Hui Zhang
2 years ago
committed by
GitHub