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#!/usr/bin/evn python3
"""Build Speech Recognition pipeline based on fairseq's wav2vec2.0 and dump it to TorchScript file.
To use this script, you need `fairseq`.
"""
import argparse
import logging
import os
from typing import Tuple
import fairseq
import torch
import torchaudio
from greedy_decoder import Decoder
from torch.utils.mobile_optimizer import optimize_for_mobile
from torchaudio.models.wav2vec2.utils.import_fairseq import import_fairseq_model
TORCH_VERSION: Tuple[int, ...] = tuple(int(x) for x in torch.__version__.split(".")[:2])
if TORCH_VERSION >= (1, 10):
import torch.ao.quantization as tq
else:
import torch.quantization as tq
_LG = logging.getLogger(__name__)
def _parse_args():
parser = argparse.ArgumentParser(
description=__doc__,
)
parser.add_argument("--model-file", required=True, help="Path to the input pretrained weight file.")
parser.add_argument(
"--dict-dir",
help=(
"Path to the directory in which `dict.ltr.txt` file is found. " "Required only when the model is finetuned."
),
)
parser.add_argument(
"--output-path",
help="Path to the directory, where the TorchScript-ed pipelines are saved.",
)
parser.add_argument(
"--test-file",
help="Path to a test audio file.",
)
parser.add_argument(
"--debug",
action="store_true",
help=(
"When enabled, individual components are separately tested "
"for the numerical compatibility and TorchScript compatibility."
),
)
parser.add_argument("--quantize", action="store_true", help="Apply quantization to model.")
parser.add_argument("--optimize-for-mobile", action="store_true", help="Apply optmization for mobile.")
return parser.parse_args()
class Loader(torch.nn.Module):
def forward(self, audio_path: str) -> torch.Tensor:
waveform, sample_rate = torchaudio.load(audio_path)
if sample_rate != 16000:
waveform = torchaudio.functional.resample(waveform, float(sample_rate), 16000.0)
return waveform
class Encoder(torch.nn.Module):
def __init__(self, encoder: torch.nn.Module):
super().__init__()
self.encoder = encoder
def forward(self, waveform: torch.Tensor) -> torch.Tensor:
result, _ = self.encoder(waveform)
return result[0]
def _get_decoder():
labels = [
"<s>",
"<pad>",
"</s>",
"<unk>",
"|",
"E",
"T",
"A",
"O",
"N",
"I",
"H",
"S",
"R",
"D",
"L",
"U",
"M",
"W",
"C",
"F",
"G",
"Y",
"P",
"B",
"V",
"K",
"'",
"X",
"J",
"Q",
"Z",
]
return Decoder(labels)
def _load_fairseq_model(input_file, data_dir=None):
overrides = {}
if data_dir:
overrides["data"] = data_dir
model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([input_file], arg_overrides=overrides)
model = model[0]
return model
def _get_model(model_file, dict_dir):
original = _load_fairseq_model(model_file, dict_dir)
model = import_fairseq_model(original.w2v_encoder)
return model
def _main():
args = _parse_args()
_init_logging(args.debug)
loader = Loader()
model = _get_model(args.model_file, args.dict_dir).eval()
encoder = Encoder(model)
decoder = _get_decoder()
_LG.info(encoder)
if args.quantize:
_LG.info("Quantizing the model")
model.encoder.transformer.pos_conv_embed.__prepare_scriptable__()
encoder = tq.quantize_dynamic(encoder, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8)
_LG.info(encoder)
# test
if args.test_file:
_LG.info("Testing with %s", args.test_file)
waveform = loader(args.test_file)
emission = encoder(waveform)
transcript = decoder(emission)
_LG.info(transcript)
torch.jit.script(loader).save(os.path.join(args.output_path, "loader.zip"))
torch.jit.script(decoder).save(os.path.join(args.output_path, "decoder.zip"))
scripted = torch.jit.script(encoder)
if args.optimize_for_mobile:
scripted = optimize_for_mobile(scripted)
scripted.save(os.path.join(args.output_path, "encoder.zip"))
def _init_logging(debug=False):
level = logging.DEBUG if debug else logging.INFO
format_ = "%(message)s" if not debug else "%(asctime)s: %(levelname)7s: %(funcName)10s: %(message)s"
logging.basicConfig(level=level, format=format_)
if __name__ == "__main__":
_main()
|
