File size: 7,186 Bytes
20b6753 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the license found in the
# MIT_LICENSE file in the root directory of this source tree.
import argparse
import logging
from argparse import Namespace
from pathlib import Path
from typing import Tuple
import torch
import torchaudio
from fairseq2.generation import NGramRepeatBlockProcessor
from seamless_communication.inference import SequenceGeneratorOptions, Translator
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)s -- %(name)s: %(message)s",
)
logger = logging.getLogger(__name__)
def add_inference_arguments(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
parser.add_argument("--task", type=str, help="Task type")
parser.add_argument(
"--tgt_lang", type=str, help="Target language to translate/transcribe into."
)
parser.add_argument(
"--src_lang",
type=str,
help="Source language, only required if input is text.",
default=None,
)
parser.add_argument(
"--output_path",
type=Path,
help="Path to save the generated audio.",
default=None,
)
parser.add_argument(
"--model_name",
type=str,
help=(
"Base model name (`seamlessM4T_medium`, "
"`seamlessM4T_large`, `seamlessM4T_v2_large`)"
),
default="seamlessM4T_v2_large",
)
parser.add_argument(
"--vocoder_name",
type=str,
help="Vocoder model name",
default="vocoder_v2",
)
# Text generation args.
parser.add_argument(
"--text_generation_beam_size",
type=int,
help="Beam size for incremental text decoding.",
default=5,
)
parser.add_argument(
"--text_generation_max_len_a",
type=int,
help="`a` in `ax + b` for incremental text decoding.",
default=1,
)
parser.add_argument(
"--text_generation_max_len_b",
type=int,
help="`b` in `ax + b` for incremental text decoding.",
default=200,
)
parser.add_argument(
"--text_generation_ngram_blocking",
type=bool,
help=(
"Enable ngram_repeat_block for incremental text decoding."
"This blocks hypotheses with repeating ngram tokens."
),
default=False,
)
parser.add_argument(
"--no_repeat_ngram_size",
type=int,
help="Size of ngram repeat block for both text & unit decoding.",
default=4,
)
# Unit generation args.
parser.add_argument(
"--unit_generation_beam_size",
type=int,
help=(
"Beam size for incremental unit decoding"
"not applicable for the NAR T2U decoder."
),
default=5,
)
parser.add_argument(
"--unit_generation_max_len_a",
type=int,
help=(
"`a` in `ax + b` for incremental unit decoding"
"not applicable for the NAR T2U decoder."
),
default=25,
)
parser.add_argument(
"--unit_generation_max_len_b",
type=int,
help=(
"`b` in `ax + b` for incremental unit decoding"
"not applicable for the NAR T2U decoder."
),
default=50,
)
parser.add_argument(
"--unit_generation_ngram_blocking",
type=bool,
help=(
"Enable ngram_repeat_block for incremental unit decoding."
"This blocks hypotheses with repeating ngram tokens."
),
default=False,
)
parser.add_argument(
"--unit_generation_ngram_filtering",
type=bool,
help=(
"If True, removes consecutive repeated ngrams"
"from the decoded unit output."
),
default=False,
)
parser.add_argument(
"--text_unk_blocking",
type=bool,
help=(
"If True, set penalty of UNK to inf in text generator "
"to block unk output."
),
default=False,
)
return parser
def set_generation_opts(
args: Namespace,
) -> Tuple[SequenceGeneratorOptions, SequenceGeneratorOptions]:
# Set text, unit generation opts.
text_generation_opts = SequenceGeneratorOptions(
beam_size=args.text_generation_beam_size,
soft_max_seq_len=(
args.text_generation_max_len_a,
args.text_generation_max_len_b,
),
)
if args.text_unk_blocking:
text_generation_opts.unk_penalty = torch.inf
if args.text_generation_ngram_blocking:
text_generation_opts.step_processor = NGramRepeatBlockProcessor(
ngram_size=args.no_repeat_ngram_size
)
unit_generation_opts = SequenceGeneratorOptions(
beam_size=args.unit_generation_beam_size,
soft_max_seq_len=(
args.unit_generation_max_len_a,
args.unit_generation_max_len_b,
),
)
if args.unit_generation_ngram_blocking:
unit_generation_opts.step_processor = NGramRepeatBlockProcessor(
ngram_size=args.no_repeat_ngram_size
)
return text_generation_opts, unit_generation_opts
def main() -> None:
parser = argparse.ArgumentParser(
description="M4T inference on supported tasks using Translator."
)
parser.add_argument("input", type=str, help="Audio WAV file path or text input.")
parser = add_inference_arguments(parser)
args = parser.parse_args()
if not args.task or not args.tgt_lang:
raise Exception(
"Please provide required arguments for evaluation - task, tgt_lang"
)
if args.task.upper() in {"S2ST", "T2ST"} and args.output_path is None:
raise ValueError("output_path must be provided to save the generated audio")
if torch.cuda.is_available():
device = torch.device("cuda:0")
dtype = torch.float16
else:
device = torch.device("cpu")
dtype = torch.float32
logger.info(f"Running inference on {device=} with {dtype=}.")
translator = Translator(args.model_name, args.vocoder_name, device, dtype=dtype)
text_generation_opts, unit_generation_opts = set_generation_opts(args)
logger.info(f"{text_generation_opts=}")
logger.info(f"{unit_generation_opts=}")
logger.info(
f"unit_generation_ngram_filtering={args.unit_generation_ngram_filtering}"
)
text_output, speech_output = translator.predict(
args.input,
args.task,
args.tgt_lang,
src_lang=args.src_lang,
text_generation_opts=text_generation_opts,
unit_generation_opts=unit_generation_opts,
unit_generation_ngram_filtering=args.unit_generation_ngram_filtering,
)
if speech_output is not None:
logger.info(f"Saving translated audio in {args.tgt_lang}")
torchaudio.save(
args.output_path,
speech_output.audio_wavs[0][0].to(torch.float32).cpu(),
sample_rate=speech_output.sample_rate,
)
logger.info(f"Translated text in {args.tgt_lang}: {text_output[0]}")
if __name__ == "__main__":
main()
|