Spaces:
Runtime error
Runtime error
File size: 7,313 Bytes
62e9ca6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
# ----------------------------------------------------------------------------
# SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data (https://arxiv.org/abs/2209.15329)
# Github source: https://github.com/microsoft/SpeechT5/tree/main/SpeechLM
# Code based on fairseq: https://github.com/facebookresearch/fairseq/tree/272c4c5197250997148fb12c0db6306035f166a4
#
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# ----------------------------------------------------------------------------
import torch
import numpy as np
import logging
from pathlib import Path
from argparse import Namespace
from fairseq.tasks import LegacyFairseqTask, register_task
from fairseq.data import Dictionary, encoders
from fairseq.data.audio.speech_to_text_joint_dataset import S2TJointDataConfig
from speechlm.unit_generator import NonAutoregressiveUnitGenerator
from speechlm.data.text_to_unit_dataset import Text2UnitDatasetCreator
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=logging.INFO,
)
logger = logging.getLogger(__name__)
@register_task("fast_text_to_unit")
class FastTextToUnitTask(LegacyFairseqTask):
@staticmethod
def add_args(parser):
parser.add_argument("data", help="manifest root path")
parser.add_argument(
"--config-yaml",
type=str,
default="config.yaml",
help="Configuration YAML filename (under manifest root)",
)
parser.add_argument(
"--max-source-positions",
default=2048,
type=int,
metavar="N",
help="max number of tokens in the source sequence",
)
parser.add_argument(
"--max-target-positions",
default=1024,
type=int,
metavar="N",
help="max number of tokens in the target sequence",
)
parser.add_argument("--n-frames-per-step", type=int, default=1)
parser.add_argument("--eos-prob-threshold", type=float, default=0.5)
parser.add_argument("--eval-inference", action="store_true")
parser.add_argument("--eval-tb-nsample", type=int, default=8)
parser.add_argument("--vocoder", type=str, default="griffin_lim")
parser.add_argument("--spec-bwd-max-iter", type=int, default=8)
def __init__(self, args, src_dict, tgt_dict):
super().__init__(args)
self.src_dict = src_dict
self.tgt_dict = tgt_dict
self.data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml)
self.speaker_to_id = self._get_speaker_to_id()
@classmethod
def setup_task(cls, args, **kwargs):
data_cfg = S2TJointDataConfig(Path(args.data) / args.config_yaml)
src_dict_path = Path(args.data) / data_cfg.src_vocab_filename
if not src_dict_path.is_file():
raise FileNotFoundError(f"Dict not found: {src_dict_path.as_posix()}")
src_dict = Dictionary.load(src_dict_path.as_posix())
logger.info(
f"Source dictionary size ({data_cfg.src_vocab_filename}): " f"{len(src_dict):,}"
)
tgt_dict_path = Path(args.data) / data_cfg.vocab_filename
if not tgt_dict_path.is_file():
raise FileNotFoundError(f"Dict not found: {tgt_dict_path.as_posix()}")
tgt_dict = Dictionary.load(tgt_dict_path.as_posix())
logger.info(
f"Target dictionary size ({data_cfg.vocab_filename}): " f"{len(tgt_dict):,}"
)
if getattr(args, "train_subset", None) is not None:
if not all(s.startswith("train") for s in args.train_subset.split(",")):
raise ValueError('Train splits should be named like "train*".')
return cls(args, src_dict, tgt_dict)
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
is_train_split = split.startswith("train")
pre_tokenizer = self.build_tokenizer(self.args)
bpe_tokenizer = self.build_bpe(self.args)
self.datasets[split] = Text2UnitDatasetCreator.from_tsv(
self.args.data,
self.data_cfg,
split,
self.src_dict,
pre_tokenizer,
bpe_tokenizer,
is_train_split=is_train_split,
epoch=epoch,
seed=self.args.seed,
n_frames_per_step=self.args.n_frames_per_step,
speaker_to_id=self.speaker_to_id,
)
@property
def target_dictionary(self):
return self.tgt_dict
@property
def source_dictionary(self):
return self.src_dict
def max_positions(self):
return self.args.max_source_positions, self.args.max_target_positions
def _get_speaker_to_id(self):
speaker_to_id = None
speaker_set_filename = self.data_cfg.config.get("speaker_set_filename")
if speaker_set_filename is not None:
speaker_set_path = Path(self.args.data) / speaker_set_filename
with open(speaker_set_path) as f:
speaker_to_id = {r.strip(): i for i, r in enumerate(f)}
return speaker_to_id
@classmethod
def get_speaker_embeddings(cls, args):
# It Will be used in FastText2UnitModel model, insdead of nn.Embedding on speaker-id, we default to use x-vectors extracted ahead.
# This is for varying the speaker information when generating units from text.
if args.speaker_to_id is not None:
embed_speaker = torch.nn.Embedding(
len(args.speaker_to_id), args.speaker_embed_dim
)
elif args.speaker_embedding_type == "x-vector":
# return LayerNorm(args.speaker_embed_dim)
return lambda x: x.unsqueeze(1)
elif args.speaker_embedding_type == "i-vector":
# return LayerNorm(args.speaker_embed_dim)
return lambda x: x
else:
embed_speaker = None
return embed_speaker
def build_model(self, cfg):
cfg.pitch_min = self.data_cfg.config["features"].get("pitch_min", None)
cfg.pitch_max = self.data_cfg.config["features"].get("pitch_max", None)
cfg.energy_min = self.data_cfg.config["features"].get("energy_min", None)
cfg.energy_max = self.data_cfg.config["features"].get("energy_max", None)
cfg.speaker_to_id = self.speaker_to_id
cfg.speaker_embedding_type = self.data_cfg.config.get("speaker_embedding_type", None)
model = super().build_model(cfg)
self.generator = None
if getattr(cfg, "eval_inference", False):
self.generator = self.build_generator([model], cfg)
return model
def build_generator(self, models, cfg, vocoder=None, **unused):
model = models[0]
assert getattr(model, "NON_AUTOREGRESSIVE") is True
return NonAutoregressiveUnitGenerator(model, vocoder, self.data_cfg)
def build_tokenizer(self, args):
logger.info(f"pre-tokenizer: {self.data_cfg.pre_tokenizer}")
return encoders.build_tokenizer(Namespace(**self.data_cfg.pre_tokenizer))
def build_bpe(self, args):
logger.info(f"tokenizer: {self.data_cfg.bpe_tokenizer}")
return encoders.build_bpe(Namespace(**self.data_cfg.bpe_tokenizer))
|