Source code for espnet2.bin.tts_inference

#!/usr/bin/env python3

"""Script to run the inference of text-to-speeech model."""

import argparse
import logging
import shutil
import sys
import time
from pathlib import Path
from typing import Any, Dict, Optional, Sequence, Tuple, Union

import numpy as np
import soundfile as sf
import torch
from packaging.version import parse as V
from typeguard import typechecked

from espnet2.fileio.npy_scp import NpyScpWriter
from espnet2.gan_tts.vits import VITS
from espnet2.tasks.tts import TTSTask
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.tts.fastspeech import FastSpeech
from espnet2.tts.fastspeech2 import FastSpeech2
from espnet2.tts.tacotron2 import Tacotron2
from espnet2.tts.transformer import Transformer
from espnet2.tts.utils import DurationCalculator
from espnet2.utils import config_argparse
from espnet2.utils.types import str2bool, str2triple_str, str_or_none
from espnet.utils.cli_utils import get_commandline_args

[docs]class Text2Speech: """Text2Speech class. Examples: >>> from espnet2.bin.tts_inference import Text2Speech >>> # Case 1: Load the local model and use Griffin-Lim vocoder >>> text2speech = Text2Speech( >>> train_config="/path/to/config.yml", >>> model_file="/path/to/model.pth", >>> ) >>> # Case 2: Load the local model and the pretrained vocoder >>> text2speech = Text2Speech.from_pretrained( >>> train_config="/path/to/config.yml", >>> model_file="/path/to/model.pth", >>> vocoder_tag="kan-bayashi/ljspeech_tacotron2", >>> ) >>> # Case 3: Load the pretrained model and use Griffin-Lim vocoder >>> text2speech = Text2Speech.from_pretrained( >>> model_tag="kan-bayashi/ljspeech_tacotron2", >>> ) >>> # Case 4: Load the pretrained model and the pretrained vocoder >>> text2speech = Text2Speech.from_pretrained( >>> model_tag="kan-bayashi/ljspeech_tacotron2", >>> vocoder_tag="parallel_wavegan/ljspeech_parallel_wavegan.v1", >>> ) >>> # Run inference and save as wav file >>> import soundfile as sf >>> wav = text2speech("Hello, World")["wav"] >>> sf.write("out.wav", wav.numpy(), text2speech.fs, "PCM_16") """ @typechecked def __init__( self, train_config: Union[Path, str, None] = None, model_file: Union[Path, str, None] = None, threshold: float = 0.5, minlenratio: float = 0.0, maxlenratio: float = 10.0, use_teacher_forcing: bool = False, use_att_constraint: bool = False, backward_window: int = 1, forward_window: int = 3, speed_control_alpha: float = 1.0, noise_scale: float = 0.667, noise_scale_dur: float = 0.8, vocoder_config: Union[Path, str, None] = None, vocoder_file: Union[Path, str, None] = None, dtype: str = "float32", device: str = "cpu", seed: int = 777, always_fix_seed: bool = False, prefer_normalized_feats: bool = False, ): """Initialize Text2Speech module.""" # setup model model, train_args = TTSTask.build_model_from_file( train_config, model_file, device ), dtype)).eval() self.device = device self.dtype = dtype self.train_args = train_args self.model = model self.tts = model.tts self.normalize = model.normalize self.feats_extract = model.feats_extract self.duration_calculator = DurationCalculator() self.preprocess_fn = TTSTask.build_preprocess_fn(train_args, False) self.use_teacher_forcing = use_teacher_forcing self.seed = seed self.always_fix_seed = always_fix_seed self.vocoder = None self.prefer_normalized_feats = prefer_normalized_feats if self.tts.require_vocoder: vocoder = TTSTask.build_vocoder_from_file( vocoder_config, vocoder_file, model, device ) if isinstance(vocoder, torch.nn.Module):, dtype)).eval() self.vocoder = vocoder"Extractor:\n{self.feats_extract}")"Normalizer:\n{self.normalize}")"TTS:\n{self.tts}") if self.vocoder is not None:"Vocoder:\n{self.vocoder}") # setup decoding config decode_conf = {} decode_conf.update(use_teacher_forcing=use_teacher_forcing) if isinstance(self.tts, (Tacotron2, Transformer)): decode_conf.update( threshold=threshold, maxlenratio=maxlenratio, minlenratio=minlenratio, ) if isinstance(self.tts, Tacotron2): decode_conf.update( use_att_constraint=use_att_constraint, forward_window=forward_window, backward_window=backward_window, ) if isinstance(self.tts, (FastSpeech, FastSpeech2, VITS)): decode_conf.update(alpha=speed_control_alpha) if isinstance(self.tts, VITS): decode_conf.update( noise_scale=noise_scale, noise_scale_dur=noise_scale_dur, ) self.decode_conf = decode_conf @torch.no_grad() @typechecked def __call__( self, text: Union[str, torch.Tensor, np.ndarray], speech: Union[torch.Tensor, np.ndarray, None] = None, durations: Union[torch.Tensor, np.ndarray, None] = None, spembs: Union[torch.Tensor, np.ndarray, None] = None, sids: Union[torch.Tensor, np.ndarray, None] = None, lids: Union[torch.Tensor, np.ndarray, None] = None, decode_conf: Optional[Dict[str, Any]] = None, ) -> Dict[str, torch.Tensor]: """Run text-to-speech.""" # check inputs if self.use_speech and speech is None: raise RuntimeError("Missing required argument: 'speech'") if self.use_sids and sids is None: raise RuntimeError("Missing required argument: 'sids'") if self.use_lids and lids is None: raise RuntimeError("Missing required argument: 'lids'") if self.use_spembs and spembs is None: raise RuntimeError("Missing required argument: 'spembs'") # prepare batch if isinstance(text, str): text = self.preprocess_fn("<dummy>", dict(text=text))["text"] batch = dict(text=text) if speech is not None: batch.update(speech=speech) if durations is not None: batch.update(durations=durations) if spembs is not None: batch.update(spembs=spembs) if sids is not None: batch.update(sids=sids) if lids is not None: batch.update(lids=lids) batch = to_device(batch, self.device) # overwrite the decode configs if provided cfg = self.decode_conf if decode_conf is not None: cfg = self.decode_conf.copy() cfg.update(decode_conf) # inference if self.always_fix_seed: set_all_random_seed(self.seed) output_dict = self.model.inference(**batch, **cfg) # calculate additional metrics if output_dict.get("att_w") is not None: duration, focus_rate = self.duration_calculator(output_dict["att_w"]) output_dict.update(duration=duration, focus_rate=focus_rate) # apply vocoder (mel-to-wav) if self.vocoder is not None: if ( self.prefer_normalized_feats or output_dict.get("feat_gen_denorm") is None ): input_feat = output_dict["feat_gen"] else: input_feat = output_dict["feat_gen_denorm"] wav = self.vocoder(input_feat) output_dict.update(wav=wav) return output_dict @property def fs(self) -> Optional[int]: """Return sampling rate.""" if hasattr(self.vocoder, "fs"): return self.vocoder.fs elif hasattr(self.tts, "fs"): return self.tts.fs else: return None @property def use_speech(self) -> bool: """Return speech is needed or not in the inference.""" return self.use_teacher_forcing or getattr(self.tts, "use_gst", False) @property def use_sids(self) -> bool: """Return sid is needed or not in the inference.""" return self.tts.spks is not None @property def use_lids(self) -> bool: """Return sid is needed or not in the inference.""" return self.tts.langs is not None @property def use_spembs(self) -> bool: """Return spemb is needed or not in the inference.""" return self.tts.spk_embed_dim is not None
[docs] @staticmethod def from_pretrained( model_tag: Optional[str] = None, vocoder_tag: Optional[str] = None, **kwargs: Optional[Any], ): """Build Text2Speech instance from the pretrained model. Args: model_tag (Optional[str]): Model tag of the pretrained models. Currently, the tags of espnet_model_zoo are supported. vocoder_tag (Optional[str]): Vocoder tag of the pretrained vocoders. Currently, the tags of parallel_wavegan are supported, which should start with the prefix "parallel_wavegan/". Returns: Text2Speech: Text2Speech instance. """ if model_tag is not None: try: from espnet_model_zoo.downloader import ModelDownloader except ImportError: logging.error( "`espnet_model_zoo` is not installed. " "Please install via `pip install -U espnet_model_zoo`." ) raise d = ModelDownloader() kwargs.update(**d.download_and_unpack(model_tag)) if vocoder_tag is not None: if vocoder_tag.startswith("parallel_wavegan/"): try: from parallel_wavegan.utils import download_pretrained_model except ImportError: logging.error( "`parallel_wavegan` is not installed. " "Please install via `pip install -U parallel_wavegan`." ) raise from parallel_wavegan import __version__ # NOTE(kan-bayashi): Filelock download is supported from 0.5.2 assert V(__version__) > V("0.5.1"), ( "Please install the latest parallel_wavegan " "via `pip install -U parallel_wavegan`." ) vocoder_tag = vocoder_tag.replace("parallel_wavegan/", "") vocoder_file = download_pretrained_model(vocoder_tag) vocoder_config = Path(vocoder_file).parent / "config.yml" kwargs.update(vocoder_config=vocoder_config, vocoder_file=vocoder_file) else: raise ValueError(f"{vocoder_tag} is unsupported format.") return Text2Speech(**kwargs)
[docs]@typechecked def inference( output_dir: str, batch_size: int, dtype: str, ngpu: int, seed: int, num_workers: int, log_level: Union[int, str], data_path_and_name_and_type: Sequence[Tuple[str, str, str]], key_file: Optional[str], train_config: Optional[str], model_file: Optional[str], model_tag: Optional[str], threshold: float, minlenratio: float, maxlenratio: float, use_teacher_forcing: bool, use_att_constraint: bool, backward_window: int, forward_window: int, speed_control_alpha: float, noise_scale: float, noise_scale_dur: float, always_fix_seed: bool, allow_variable_data_keys: bool, vocoder_config: Optional[str], vocoder_file: Optional[str], vocoder_tag: Optional[str], ): """Run text-to-speech inference.""" if batch_size > 1: raise NotImplementedError("batch decoding is not implemented") if ngpu > 1: raise NotImplementedError("only single GPU decoding is supported") logging.basicConfig( level=log_level, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) if ngpu >= 1: device = "cuda" else: device = "cpu" # 1. Set random-seed set_all_random_seed(seed) # 2. Build model text2speech_kwargs = dict( train_config=train_config, model_file=model_file, threshold=threshold, maxlenratio=maxlenratio, minlenratio=minlenratio, use_teacher_forcing=use_teacher_forcing, use_att_constraint=use_att_constraint, backward_window=backward_window, forward_window=forward_window, speed_control_alpha=speed_control_alpha, noise_scale=noise_scale, noise_scale_dur=noise_scale_dur, vocoder_config=vocoder_config, vocoder_file=vocoder_file, dtype=dtype, device=device, seed=seed, always_fix_seed=always_fix_seed, ) text2speech = Text2Speech.from_pretrained( model_tag=model_tag, vocoder_tag=vocoder_tag, **text2speech_kwargs, ) # 3. Build data-iterator if not text2speech.use_speech: data_path_and_name_and_type = list( filter(lambda x: x[1] != "speech", data_path_and_name_and_type) ) loader = TTSTask.build_streaming_iterator( data_path_and_name_and_type, dtype=dtype, batch_size=batch_size, key_file=key_file, num_workers=num_workers, preprocess_fn=TTSTask.build_preprocess_fn(text2speech.train_args, False), collate_fn=TTSTask.build_collate_fn(text2speech.train_args, False), allow_variable_data_keys=allow_variable_data_keys, inference=True, ) # 6. Start for-loop output_dir = Path(output_dir) (output_dir / "norm").mkdir(parents=True, exist_ok=True) (output_dir / "denorm").mkdir(parents=True, exist_ok=True) (output_dir / "speech_shape").mkdir(parents=True, exist_ok=True) (output_dir / "wav").mkdir(parents=True, exist_ok=True) (output_dir / "att_ws").mkdir(parents=True, exist_ok=True) (output_dir / "probs").mkdir(parents=True, exist_ok=True) (output_dir / "durations").mkdir(parents=True, exist_ok=True) (output_dir / "focus_rates").mkdir(parents=True, exist_ok=True) # Lazy load to avoid the backend error import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from matplotlib.ticker import MaxNLocator with NpyScpWriter( output_dir / "norm", output_dir / "norm/feats.scp", ) as norm_writer, NpyScpWriter( output_dir / "denorm", output_dir / "denorm/feats.scp" ) as denorm_writer, open( output_dir / "speech_shape/speech_shape", "w" ) as shape_writer, open( output_dir / "durations/durations", "w" ) as duration_writer, open( output_dir / "focus_rates/focus_rates", "w" ) as focus_rate_writer: for idx, (keys, batch) in enumerate(loader, 1): assert isinstance(batch, dict), type(batch) assert all(isinstance(s, str) for s in keys), keys _bs = len(next(iter(batch.values()))) assert _bs == 1, _bs # Change to single sequence and remove *_length # because inference() requires 1-seq, not mini-batch. batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} start_time = time.perf_counter() output_dict = text2speech(**batch) key = keys[0] insize = next(iter(batch.values())).size(0) + 1 if output_dict.get("feat_gen") is not None: # standard text2mel model case feat_gen = output_dict["feat_gen"] "inference speed = {:.1f} frames / sec.".format( int(feat_gen.size(0)) / (time.perf_counter() - start_time) ) )"{key} (size:{insize}->{feat_gen.size(0)})") if feat_gen.size(0) == insize * maxlenratio: logging.warning(f"output length reaches maximum length ({key}).") norm_writer[key] = output_dict["feat_gen"].cpu().numpy() shape_writer.write( f"{key} " + ",".join(map(str, output_dict["feat_gen"].shape)) + "\n" ) if output_dict.get("feat_gen_denorm") is not None: denorm_writer[key] = output_dict["feat_gen_denorm"].cpu().numpy() else: # end-to-end text2wav model case wav = output_dict["wav"] "inference speed = {:.1f} points / sec.".format( int(wav.size(0)) / (time.perf_counter() - start_time) ) )"{key} (size:{insize}->{wav.size(0)})") if output_dict.get("duration") is not None: # Save duration and fucus rates duration_writer.write( f"{key} " + " ".join(map(str, output_dict["duration"].long().cpu().numpy())) + "\n" ) if output_dict.get("focus_rate") is not None: focus_rate_writer.write( f"{key} {float(output_dict['focus_rate']):.5f}\n" ) if output_dict.get("att_w") is not None: # Plot attention weight att_w = output_dict["att_w"].cpu().numpy() if att_w.ndim == 2: att_w = att_w[None][None] elif att_w.ndim != 4: raise RuntimeError(f"Must be 2 or 4 dimension: {att_w.ndim}") w, h = plt.figaspect(att_w.shape[0] / att_w.shape[1]) fig = plt.Figure( figsize=( w * 1.3 * min(att_w.shape[0], 2.5), h * 1.3 * min(att_w.shape[1], 2.5), ) ) fig.suptitle(f"{key}") axes = fig.subplots(att_w.shape[0], att_w.shape[1]) if len(att_w) == 1: axes = [[axes]] for ax, att_w in zip(axes, att_w): for ax_, att_w_ in zip(ax, att_w): ax_.imshow(att_w_.astype(np.float32), aspect="auto") ax_.set_xlabel("Input") ax_.set_ylabel("Output") ax_.xaxis.set_major_locator(MaxNLocator(integer=True)) ax_.yaxis.set_major_locator(MaxNLocator(integer=True)) fig.set_tight_layout({"rect": [0, 0.03, 1, 0.95]}) fig.savefig(output_dir / f"att_ws/{key}.png") fig.clf() if output_dict.get("prob") is not None: # Plot stop token prediction prob = output_dict["prob"].cpu().numpy() fig = plt.Figure() ax = fig.add_subplot(1, 1, 1) ax.plot(prob) ax.set_title(f"{key}") ax.set_xlabel("Output") ax.set_ylabel("Stop probability") ax.set_ylim(0, 1) ax.grid(which="both") fig.set_tight_layout(True) fig.savefig(output_dir / f"probs/{key}.png") fig.clf() if output_dict.get("wav") is not None: # TODO(kamo): Write scp sf.write( f"{output_dir}/wav/{key}.wav", output_dict["wav"].cpu().numpy(), text2speech.fs, "PCM_16", ) # remove files if those are not included in output dict if output_dict.get("feat_gen") is None: shutil.rmtree(output_dir / "norm") if output_dict.get("feat_gen_denorm") is None: shutil.rmtree(output_dir / "denorm") if output_dict.get("att_w") is None: shutil.rmtree(output_dir / "att_ws") if output_dict.get("duration") is None: shutil.rmtree(output_dir / "durations") if output_dict.get("focus_rate") is None: shutil.rmtree(output_dir / "focus_rates") if output_dict.get("prob") is None: shutil.rmtree(output_dir / "probs") if output_dict.get("wav") is None: shutil.rmtree(output_dir / "wav")
[docs]def get_parser(): """Get argument parser.""" parser = config_argparse.ArgumentParser( description="TTS inference", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) # Note(kamo): Use "_" instead of "-" as separator. # "-" is confusing if written in yaml. parser.add_argument( "--log_level", type=lambda x: x.upper(), default="INFO", choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"), help="The verbose level of logging", ) parser.add_argument( "--output_dir", type=str, required=True, help="The path of output directory", ) parser.add_argument( "--ngpu", type=int, default=0, help="The number of gpus. 0 indicates CPU mode", ) parser.add_argument( "--seed", type=int, default=0, help="Random seed", ) parser.add_argument( "--dtype", default="float32", choices=["float16", "float32", "float64"], help="Data type", ) parser.add_argument( "--num_workers", type=int, default=1, help="The number of workers used for DataLoader", ) parser.add_argument( "--batch_size", type=int, default=1, help="The batch size for inference", ) group = parser.add_argument_group("Input data related") group.add_argument( "--data_path_and_name_and_type", type=str2triple_str, required=True, action="append", ) group.add_argument( "--key_file", type=str_or_none, ) group.add_argument( "--allow_variable_data_keys", type=str2bool, default=False, ) group = parser.add_argument_group("The model configuration related") group.add_argument( "--train_config", type=str, help="Training configuration file", ) group.add_argument( "--model_file", type=str, help="Model parameter file", ) group.add_argument( "--model_tag", type=str, help="Pretrained model tag. If specify this option, train_config and " "model_file will be overwritten", ) group = parser.add_argument_group("Decoding related") group.add_argument( "--maxlenratio", type=float, default=10.0, help="Maximum length ratio in decoding", ) group.add_argument( "--minlenratio", type=float, default=0.0, help="Minimum length ratio in decoding", ) group.add_argument( "--threshold", type=float, default=0.5, help="Threshold value in decoding", ) group.add_argument( "--use_att_constraint", type=str2bool, default=False, help="Whether to use attention constraint", ) group.add_argument( "--backward_window", type=int, default=1, help="Backward window value in attention constraint", ) group.add_argument( "--forward_window", type=int, default=3, help="Forward window value in attention constraint", ) group.add_argument( "--use_teacher_forcing", type=str2bool, default=False, help="Whether to use teacher forcing", ) parser.add_argument( "--speed_control_alpha", type=float, default=1.0, help="Alpha in FastSpeech to change the speed of generated speech", ) parser.add_argument( "--noise_scale", type=float, default=0.667, help="Noise scale parameter for the flow in vits", ) parser.add_argument( "--noise_scale_dur", type=float, default=0.8, help="Noise scale parameter for the stochastic duration predictor in vits", ) group.add_argument( "--always_fix_seed", type=str2bool, default=False, help="Whether to always fix seed", ) group = parser.add_argument_group("Vocoder related") group.add_argument( "--vocoder_config", type=str_or_none, help="Vocoder configuration file", ) group.add_argument( "--vocoder_file", type=str_or_none, help="Vocoder parameter file", ) group.add_argument( "--vocoder_tag", type=str, help="Pretrained vocoder tag. If specify this option, vocoder_config and " "vocoder_file will be overwritten", ) return parser
[docs]def main(cmd=None): """Run TTS model inference.""" print(get_commandline_args(), file=sys.stderr) parser = get_parser() args = parser.parse_args(cmd) kwargs = vars(args) kwargs.pop("config", None) inference(**kwargs)
if __name__ == "__main__": main()