Source code for espnet2.bin.enh_inference

#!/usr/bin/env python3
import argparse
import logging
import sys
from itertools import chain
from pathlib import Path
from typing import Any, List, Optional, Sequence, Tuple, Union

import humanfriendly
import numpy as np
import torch
import torchaudio
import yaml
from tqdm import trange
from typeguard import typechecked

from espnet2.enh.diffusion_enh import ESPnetDiffusionModel
from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainMSE
from espnet2.enh.loss.criterions.time_domain import SISNRLoss
from espnet2.enh.loss.wrappers.pit_solver import PITSolver
from espnet2.enh.separator.bsrnn_separator import BSRNNSeparator
from espnet2.enh.separator.tfgridnetv3_separator import TFGridNetV3
from espnet2.enh.separator.uses_separator import USESSeparator
from espnet2.fileio.sound_scp import SoundScpWriter
from espnet2.tasks.enh import EnhancementTask
from espnet2.tasks.enh_s2t import EnhS2TTask
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.train.abs_espnet_model import AbsESPnetModel
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

EPS = torch.finfo(torch.get_default_dtype()).eps

[docs]def get_train_config(train_config, model_file=None): if train_config is None: assert model_file is not None, ( "The argument 'model_file' must be provided " "if the argument 'train_config' is not specified." ) train_config = Path(model_file).parent / "config.yaml" else: train_config = Path(train_config) return train_config
[docs]def recursive_dict_update(dict_org, dict_patch, verbose=False, log_prefix=""): """Update `dict_org` with `dict_patch` in-place recursively.""" for key, value in dict_patch.items(): if key not in dict_org: if verbose: "Overwriting config: [{}{}]: None -> {}".format( log_prefix, key, value ) ) dict_org[key] = value elif isinstance(value, dict): recursive_dict_update( dict_org[key], value, verbose=verbose, log_prefix=f"{key}." ) else: if verbose and dict_org[key] != value: "Overwriting config: [{}{}]: {} -> {}".format( log_prefix, key, dict_org[key], value ) ) dict_org[key] = value
[docs]def build_model_from_args_and_file(task, args, model_file, device): model = task.build_model(args) if not isinstance(model, AbsESPnetModel): raise RuntimeError( f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}" ) if model_file is not None: if device == "cuda": # NOTE(kamo): "cuda" for torch.load always indicates cuda:0 # in PyTorch<=1.4 device = f"cuda:{torch.cuda.current_device()}" model.load_state_dict(torch.load(model_file, map_location=device)) return model
[docs]class SeparateSpeech: """SeparateSpeech class Examples: >>> import soundfile >>> separate_speech = SeparateSpeech("enh_config.yml", "enh.pth") >>> audio, rate ="speech.wav") >>> separate_speech(audio) [separated_audio1, separated_audio2, ...] """ @typechecked def __init__( self, train_config: Union[Path, str, None] = None, model_file: Union[Path, str, None] = None, inference_config: Union[Path, str, None] = None, segment_size: Optional[float] = None, hop_size: Optional[float] = None, normalize_segment_scale: bool = False, show_progressbar: bool = False, ref_channel: Optional[int] = None, normalize_output_wav: bool = False, device: str = "cpu", dtype: str = "float32", enh_s2t_task: bool = False, ): task = EnhancementTask if not enh_s2t_task else EnhS2TTask # 1. Build Enh model self.sfi_processing = False # sampling-frequency-independent (SFI) if inference_config is None: enh_model, enh_train_args = task.build_model_from_file( train_config, model_file, device ) else: # Overwrite model attributes train_config = get_train_config(train_config, model_file=model_file) with"r", encoding="utf-8") as f: train_args = yaml.safe_load(f) with Path(inference_config).open("r", encoding="utf-8") as f: infer_args = yaml.safe_load(f) if enh_s2t_task: arg_list = ("enh_encoder", "enh_separator", "enh_decoder") else: arg_list = ("encoder", "separator", "decoder") supported_keys = list(chain(*[[k, k + "_conf"] for k in arg_list])) for k in infer_args.keys(): if k not in supported_keys: raise ValueError( "Only the following top-level keys are supported: %s" % ", ".join(supported_keys) ) recursive_dict_update(train_args, infer_args, verbose=True) enh_train_args = argparse.Namespace(**train_args) enh_model = build_model_from_args_and_file( task, enh_train_args, model_file, device ) if enh_s2t_task: enh_model = enh_model.enh_model, dtype)).eval() if isinstance( enh_model.separator, ((BSRNNSeparator, USESSeparator, TFGridNetV3)) ): self.sfi_processing = True self.device = device self.dtype = dtype self.enh_train_args = enh_train_args self.enh_model = enh_model # only used when processing long speech, i.e. # segment_size is not None and hop_size is not None self.segment_size = segment_size self.hop_size = hop_size self.normalize_segment_scale = normalize_segment_scale self.normalize_output_wav = normalize_output_wav self.show_progressbar = show_progressbar self.num_spk = enh_model.num_spk task = "enhancement" if self.num_spk == 1 else "separation" # reference channel for processing multi-channel speech if ref_channel is not None: "Overwrite enh_model.separator.ref_channel with {}".format(ref_channel) ) enh_model.separator.ref_channel = ref_channel if hasattr(enh_model.separator, "beamformer"): enh_model.separator.beamformer.ref_channel = ref_channel self.ref_channel = ref_channel else: self.ref_channel = enh_model.ref_channel self.segmenting = segment_size is not None and hop_size is not None if self.segmenting:"Perform segment-wise speech %s" % task) "Segment length = {} sec, hop length = {} sec".format( segment_size, hop_size ) ) else:"Perform direct speech %s on the input" % task) @torch.no_grad() @typechecked def __call__( self, speech_mix: Union[torch.Tensor, np.ndarray], fs: int = 8000, **kwargs ) -> List[Union[torch.Tensor, np.array]]: """Inference Args: speech_mix: Input speech data (Batch, Nsamples [, Channels]) fs: sample rate Returns: [separated_audio1, separated_audio2, ...] """ # Input as audio signal if isinstance(speech_mix, np.ndarray): speech_mix = torch.as_tensor(speech_mix) assert speech_mix.dim() > 1, speech_mix.size() batch_size = speech_mix.size(0) speech_mix =, self.dtype)) # lengths: (B,) lengths = speech_mix.new_full( [batch_size], dtype=torch.long, fill_value=speech_mix.size(1) ) lengths0 = lengths if self.sfi_processing: fs_ = fs else: fs_ = None if self.enh_model.always_forward_in_48k: lengths = lengths.new_tensor( [ torchaudio.functional.resample( torch.randn(L, device="meta"), fs, 48000 ).size(0) for L in lengths ] ) speech_mix = torchaudio.functional.resample(speech_mix, fs, 48000) # a. To device speech_mix = to_device(speech_mix, device=self.device) lengths = to_device(lengths, device=self.device) ################################### # Normalize the signal variance if getattr(self.enh_model, "normalize_variance_per_ch", False): dim = 1 mix_std_ = torch.std(speech_mix, dim=dim, keepdim=True) speech_mix = speech_mix / mix_std_ # RMS normalization elif getattr(self.enh_model, "normalize_variance", False): if speech_mix.ndim > 2: dim = (1, 2) else: dim = 1 mix_std_ = torch.std(speech_mix, dim=dim, keepdim=True) speech_mix = speech_mix / mix_std_ # RMS normalization category = kwargs.get("utt2category", None) if ( self.enh_model.categories and category is not None and category[0].item() not in self.enh_model.categories ): raise ValueError(f"Category '{category}' is not listed in self.categories") additional = {} if category is not None and self.enh_model.categories: cat = self.enh_model.categories[category[0].item()] print(f"category: {cat}", flush=True) if cat.endswith("_reverb"): additional["mode"] = "dereverb" else: additional["mode"] = "no_dereverb" if self.segmenting and lengths[0] > self.segment_size * fs: # Segment-wise speech enhancement/separation overlap_length = int(np.round(fs * (self.segment_size - self.hop_size))) num_segments = int( np.ceil((speech_mix.size(1) - overlap_length) / (self.hop_size * fs)) ) t = T = int(self.segment_size * fs) pad_shape = speech_mix[:, :T].shape enh_waves = [] range_ = trange if self.show_progressbar else range for i in range_(num_segments): st = int(i * self.hop_size * fs) en = st + T if en >= lengths[0]: # en - st < T (last segment) en = lengths[0] speech_seg = speech_mix.new_zeros(pad_shape) t = en - st speech_seg[:, :t] = speech_mix[:, st:en] else: t = T speech_seg = speech_mix[:, st:en] # B x T [x C] lengths_seg = speech_mix.new_full( [batch_size], dtype=torch.long, fill_value=T ) # b. Enhancement/Separation Forward feats, f_lens = self.enh_model.encoder(speech_seg, lengths_seg, fs=fs_) if isinstance(self.enh_model, ESPnetDiffusionModel): feats = [self.enh_model.enhance(feats)] else: feats, _, _ = self.enh_model.separator(feats, f_lens, additional) processed_wav = [ self.enh_model.decoder(f, lengths_seg, fs=fs_)[0] for f in feats ] if speech_seg.dim() > 2: # multi-channel speech speech_seg_ = speech_seg[:, self.ref_channel] else: speech_seg_ = speech_seg if self.normalize_segment_scale: # normalize the scale to match the input mixture scale mix_energy = torch.sqrt( torch.mean(speech_seg_[:, :t].pow(2), dim=1, keepdim=True) ) enh_energy = torch.sqrt( torch.mean( sum(processed_wav)[:, :t].pow(2), dim=1, keepdim=True ) ) processed_wav = [ w * (mix_energy / enh_energy) for w in processed_wav ] # List[torch.Tensor(num_spk, B, T)] enh_waves.append(torch.stack(processed_wav, dim=0)) # c. Stitch the enhanced segments together waves = enh_waves[0] for i in range(1, num_segments): # permutation between separated streams in last and current segments perm = self.cal_permumation( waves[:, :, -overlap_length:], enh_waves[i][:, :, :overlap_length], criterion="si_snr", ) # repermute separated streams in current segment for batch in range(batch_size): enh_waves[i][:, batch] = enh_waves[i][perm[batch], batch] if i == num_segments - 1: enh_waves[i][:, :, t:] = 0 enh_waves_res_i = enh_waves[i][:, :, overlap_length:t] else: enh_waves_res_i = enh_waves[i][:, :, overlap_length:] # overlap-and-add (average over the overlapped part) waves[:, :, -overlap_length:] = ( waves[:, :, -overlap_length:] + enh_waves[i][:, :, :overlap_length] ) / 2 # concatenate the residual parts of the later segment waves =[waves, enh_waves_res_i], dim=2) # ensure the stitched length is same as input assert waves.size(2) == speech_mix.size(1), (waves.shape, speech_mix.shape) waves = torch.unbind(waves, dim=0) else: # b. Enhancement/Separation Forward feats, f_lens = self.enh_model.encoder(speech_mix, lengths, fs=fs_) if isinstance(self.enh_model, ESPnetDiffusionModel): feats = [self.enh_model.enhance(feats)] else: feats, _, _ = self.enh_model.separator(feats, f_lens, additional) waves = [self.enh_model.decoder(f, lengths, fs=fs_)[0] for f in feats] ################################### # De-normalize the signal variance if getattr(self.enh_model, "normalize_variance_per_ch", False): if mix_std_.ndim > 2: mix_std_ = mix_std_[:, :, self.ref_channel] waves = [w * mix_std_ for w in waves] elif getattr(self.enh_model, "normalize_variance", False): if mix_std_.ndim > 2: mix_std_ = mix_std_.squeeze(2) waves = [w * mix_std_ for w in waves] if not self.sfi_processing and self.enh_model.always_forward_in_48k: waves = [ torchaudio.functional.resample(sp, 48000, fs)[..., : lengths0.max()] for sp in waves ] assert len(waves) == self.num_spk, len(waves) == self.num_spk assert len(waves[0]) == batch_size, (len(waves[0]), batch_size) if self.normalize_output_wav: waves = [ (w / abs(w).max(dim=1, keepdim=True)[0] * 0.9).cpu().numpy() for w in waves ] # list[(batch, sample)] else: waves = [w.cpu().numpy() for w in waves] return waves
[docs] @torch.no_grad() def cal_permumation(self, ref_wavs, enh_wavs, criterion="si_snr"): """Calculate the permutation between seaprated streams in two adjacent segments. Args: ref_wavs (List[torch.Tensor]): [(Batch, Nsamples)] enh_wavs (List[torch.Tensor]): [(Batch, Nsamples)] criterion (str): one of ("si_snr", "mse", "corr) Returns: perm (torch.Tensor): permutation for enh_wavs (Batch, num_spk) """ criterion_class = {"si_snr": SISNRLoss, "mse": FrequencyDomainMSE}[criterion] pit_solver = PITSolver(criterion=criterion_class()) _, _, others = pit_solver(ref_wavs, enh_wavs) perm = others["perm"] return perm
[docs] @staticmethod def from_pretrained( model_tag: Optional[str] = None, **kwargs: Optional[Any], ): """Build SeparateSpeech 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. Returns: SeparateSpeech: SeparateSpeech 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)) return SeparateSpeech(**kwargs)
[docs]def humanfriendly_or_none(value: str): if value in ("none", "None", "NONE"): return None return humanfriendly.parse_size(value)
[docs]@typechecked def inference( output_dir: str, batch_size: int, dtype: str, fs: int, 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], inference_config: Optional[str], allow_variable_data_keys: bool, segment_size: Optional[float], hop_size: Optional[float], normalize_segment_scale: bool, show_progressbar: bool, ref_channel: Optional[int], normalize_output_wav: bool, enh_s2t_task: bool, ): 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 separate_speech separate_speech_kwargs = dict( train_config=train_config, model_file=model_file, inference_config=inference_config, segment_size=segment_size, hop_size=hop_size, normalize_segment_scale=normalize_segment_scale, show_progressbar=show_progressbar, ref_channel=ref_channel, normalize_output_wav=normalize_output_wav, device=device, dtype=dtype, enh_s2t_task=enh_s2t_task, ) separate_speech = SeparateSpeech.from_pretrained( model_tag=model_tag, **separate_speech_kwargs, ) # 3. Build data-iterator loader = EnhancementTask.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=EnhancementTask.build_preprocess_fn( separate_speech.enh_train_args, False ), collate_fn=EnhancementTask.build_collate_fn( separate_speech.enh_train_args, False ), allow_variable_data_keys=allow_variable_data_keys, inference=True, ) # 4. Start for-loop output_dir: Path = Path(output_dir).expanduser().resolve() writers = [] for i in range(separate_speech.num_spk): writers.append( SoundScpWriter(f"{output_dir}/wavs/{i + 1}", f"{output_dir}/spk{i + 1}.scp") ) import tqdm for i, (keys, batch) in tqdm.tqdm(enumerate(loader)):"[{i}] Enhancing {keys}") assert isinstance(batch, dict), type(batch) assert all(isinstance(s, str) for s in keys), keys _bs = len(next(iter(batch.values()))) assert len(keys) == _bs, f"{len(keys)} != {_bs}" batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")} if "utt2fs" in batch: # All samples must have the same sampling rate assert all([fs_ == batch["utt2fs"][0].item() for fs_ in batch["utt2fs"]]) fs_ = batch.pop("utt2fs")[0].item()"Swichting to fs={fs_}Hz") else: fs_ = fs waves = separate_speech(**batch, fs=fs_) for spk, w in enumerate(waves): for b in range(batch_size): writers[spk][keys[b]] = fs_, w[b] for writer in writers: writer.close()
[docs]def get_parser(): parser = config_argparse.ArgumentParser( description="Frontend 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) 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( "--fs", type=humanfriendly_or_none, default=8000, help="Sampling rate" ) parser.add_argument( "--num_workers", type=int, default=1, help="The number of workers used for DataLoader", ) 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("Output data related") group.add_argument( "--normalize_output_wav", type=str2bool, default=False, help="Whether to normalize the predicted wav to [-1~1]", ) 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.add_argument( "--inference_config", type=str_or_none, default=None, help="Optional configuration file for overwriting enh model attributes " "during inference", ) group.add_argument( "--enh_s2t_task", type=str2bool, default=False, help="enhancement and asr joint model", ) group = parser.add_argument_group("Data loading related") group.add_argument( "--batch_size", type=int, default=1, help="The batch size for inference", ) group = parser.add_argument_group("SeparateSpeech related") group.add_argument( "--segment_size", type=float, default=None, help="Segment length in seconds for segment-wise speech enhancement/separation", ) group.add_argument( "--hop_size", type=float, default=None, help="Hop length in seconds for segment-wise speech enhancement/separation", ) group.add_argument( "--normalize_segment_scale", type=str2bool, default=True, help="Whether to normalize the energy of the separated streams in each segment", ) group.add_argument( "--show_progressbar", type=str2bool, default=False, help="Whether to show a progress bar when performing segment-wise speech " "enhancement/separation", ) group.add_argument( "--ref_channel", type=int, default=None, help="If not None, this will overwrite the ref_channel defined in the " "separator module (for multi-channel speech processing)", ) return parser
[docs]def main(cmd=None): 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()