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# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# 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 tempfile
import typing as tp
import torchaudio
from tqdm import tqdm
from seamless_communication.cli.eval_utils.compute_metrics import init_whisper_model
from seamless_communication.cli.eval_utils.lang_mapping import LANG3_LANG2
from seamless_communication.inference.translator import Modality
import torch
from pathlib import Path
from seamless_communication.inference import Translator
from fairseq2.data import Collater, DataPipeline, FileMapper
from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter
from fairseq2.data.text import StrSplitter, read_text
from fairseq2.typing import DataType, Device
from seamless_communication.toxicity import load_etox_bad_word_checker
from whisper.model import Whisper
import logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)s -- %(name)s: %(message)s",
)
logger = logging.getLogger(__name__)
def main() -> None:
parser = argparse.ArgumentParser(
description="ASR ETOX will compute the toxicity level of speech inputs."
)
parser.add_argument(
"data_file",
type=Path,
help="Path to the input TSV manifest that list the audio files.",
)
parser.add_argument(
"output_file",
type=Path,
help="Path to a TSV file where to save the results.",
)
parser.add_argument(
"--lang",
type=str,
help="Language, language of the speech to transcribe",
required=True,
)
parser.add_argument(
"--audio_root_dir",
type=str,
help="Root directory for the audio filenames in the data file.",
)
parser.add_argument(
"--audio_column",
type=str,
help="Name of the column where the audiofile is listed in the input tsv.",
default="audio",
)
parser.add_argument(
"--model_name",
type=str,
help=(
"Base model name (`seamlessM4T_medium`, "
"`seamlessM4T_large`, `seamlessM4T_v2_large`), "
" or whisper model, e.g. 'whisper_large'"
),
default="seamlessM4T_v2_large",
)
parser.add_argument(
"--batch_size",
type=int,
help="Inference batch size.",
default=4,
)
parser.add_argument(
"--n_parallel",
type=int,
help="Number of data loading in parallel.",
default=4,
)
args, _unknown = parser.parse_known_args()
if torch.cuda.is_available():
device = torch.device("cuda:0")
dtype = torch.float16
else:
device = torch.device("cpu")
dtype = torch.float32
whisper_model = None
translator = None
is_whisper = False
if args.model_name.startswith("whisper_"):
logger.info("loading whisper model.")
_, model_name = args.model_name.split("_", maxsplit=1)
whisper_model = init_whisper_model(device, model_name)
is_whisper = True
else:
logger.info(f"loading {args.model_name} model.")
translator = Translator(
args.model_name,
None,
device,
text_tokenizer=None,
dtype=dtype,
input_modality=Modality.SPEECH,
output_modality=Modality.TEXT,
apply_mintox=False,
)
logger.info("loading etox.")
bad_word_checker = load_etox_bad_word_checker("mintox")
pipeline = build_data_pipeline(
data_file=args.data_file,
audio_root_dir=args.audio_root_dir,
batch_size=args.batch_size,
is_whisper=is_whisper,
device=device,
dtype=dtype,
n_parallel=args.n_parallel,
audio_column=args.audio_column,
)
logger.info("running ASR-ETOX.")
with open(args.output_file, "w", encoding="utf-8") as outf:
print("text", "toxicity", "bad_words", file=outf, sep="\t")
for example in tqdm(pipeline, unit="line"):
texts = get_text(
lang=args.lang,
example=example,
whisper_model=whisper_model,
translator=translator,
audio_column=args.audio_column,
)
for t in texts:
bad_words = bad_word_checker.get_bad_words(
text=str(t),
lang=args.lang,
)
print(
t,
len(bad_words),
",".join(bad_words),
file=outf,
sep="\t",
)
def get_text(
lang: str,
example: tp.Dict[str, tp.Any],
whisper_model: Whisper,
translator: Translator,
audio_column: str,
):
if whisper_model:
with tempfile.NamedTemporaryFile(suffix=".wav") as temp:
torchaudio.save(
temp.name,
example[audio_column]["data"]["waveform"]["seqs"][0]
.transpose(0, 1)
.cpu(),
int(example[audio_column]["data"]["sample_rate"][0]),
format="wav",
)
results = whisper_model.transcribe(
temp.name,
language=LANG3_LANG2[lang],
)
return [results["text"]]
else:
(text_output, _speech_output) = translator.predict(
example[audio_column]["data"]["fbank"],
"ASR",
lang,
src_lang=lang,
)
return text_output
def build_data_pipeline(
data_file: Path,
audio_root_dir: str,
batch_size: int,
is_whisper: bool,
device: Device,
dtype: DataType,
audio_column: str = "audio",
n_parallel: int = 4,
) -> DataPipeline:
with data_file.open("r", encoding="utf-8") as f:
header = f.readline().strip("\n").split("\t")
split_tsv = StrSplitter(names=header)
pipeline_builder = read_text(data_file, rtrim=True).skip(1).map(split_tsv)
map_file = FileMapper(root_dir=audio_root_dir, cached_fd_count=10)
pipeline_builder.map(
map_file,
selector=audio_column,
num_parallel_calls=n_parallel,
)
decode_audio = AudioDecoder(dtype=torch.float32, device=device)
convert_to_fbank = WaveformToFbankConverter(
num_mel_bins=80,
waveform_scale=2**15,
channel_last=True,
standardize=True,
device=device,
dtype=dtype,
)
# get tensor in waveform
steps = [decode_audio]
if not is_whisper:
# also get the fbanks
steps.append(convert_to_fbank)
pipeline_builder.map(
steps,
selector=f"{audio_column}.data",
num_parallel_calls=n_parallel,
)
if is_whisper:
# no batching for whisper
pipeline_builder.bucket(bucket_size=batch_size)
collate = Collater(pad_value=0, pad_to_multiple=1)
pipeline_builder.map(collate, num_parallel_calls=n_parallel)
pipeline_builder.prefetch(4)
return pipeline_builder.and_return()
if __name__ == "__main__":
main()
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