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Fangjun Kuang 2023-09-27 23:23:48 +08:00
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236
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_H.py
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#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
"""
This file shows how to use a torchscript model for decoding with H
on CPU using OpenFST and decoders from kaldi.
Usage:
./conformer_ctc/jit_pretrained_decode_with_H.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
--H ./data/lang_char/H.fst \
--tokens ./data/lang_char/tokens.txt \
./BAC009S0764W0121.wav \
./BAC009S0764W0122.wav \
./BAC009S0764W0123.wav
Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
you can use ./export.py --jit 1
"""
import argparse
import logging
import math
from typing import Dict, List
import kaldi_hmm_gmm
import kaldifeat
import kaldifst
import torch
import torchaudio
from kaldi_hmm_gmm import DecodableCtc, FasterDecoder, FasterDecoderOptions
from torch.nn.utils.rnn import pad_sequence
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--nn-model",
type=str,
required=True,
help="""Path to the torchscript model.
You can use ./conformer_ctc/export.py --jit 1
to obtain it
""",
)
parser.add_argument(
"--tokens",
type=str,
required=True,
help="Path to tokens.txt",
)
parser.add_argument("--H", type=str, required=True, help="Path to H.fst")
parser.add_argument(
"sound_files",
type=str,
nargs="+",
help="The input sound file(s) to transcribe. "
"Supported formats are those supported by torchaudio.load(). "
"For example, wav and flac are supported. ",
)
return parser
def read_tokens(tokens_txt: str) -> Dict[int, str]:
id2token = dict()
with open(tokens_txt, encoding="utf-8") as f:
for line in f:
token, idx = line.strip().split()
id2token[int(idx)] = token
return id2token
def read_sound_files(
filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
"""Read a list of sound files into a list 1-D float32 torch tensors.
Args:
filenames:
A list of sound filenames.
expected_sample_rate:
The expected sample rate of the sound files.
Returns:
Return a list of 1-D float32 torch tensors.
"""
ans = []
for f in filenames:
wave, sample_rate = torchaudio.load(f)
if sample_rate != expected_sample_rate:
wave = torchaudio.functional.resample(
wave,
orig_freq=sample_rate,
new_freq=expected_sample_rate,
)
# We use only the first channel
ans.append(wave[0].contiguous())
return ans
def decode(
filename: str,
nnet_output: torch.Tensor,
H: kaldifst,
id2token: Dict[int, str],
) -> List[str]:
"""
Args:
filename:
Path to the filename for decoding. Used for debugging.
nnet_output:
A 2-D float32 tensor of shape (num_frames, vocab_size). It
contains output from log_softmax.
H:
The H graph.
id2token:
A map mapping token ID to token string.
Returns:
Return a list of decoded tokens.
"""
logging.info(f"{filename}, {nnet_output.shape}")
decodable = DecodableCtc(nnet_output.cpu())
decoder_opts = FasterDecoderOptions(max_active=3000)
decoder = FasterDecoder(H, decoder_opts)
decoder.decode(decodable)
if not decoder.reached_final():
print(f"failed to decode {filename}")
return [""]
ok, best_path = decoder.get_best_path()
(
ok,
isymbols_out,
osymbols_out,
total_weight,
) = kaldifst.get_linear_symbol_sequence(best_path)
if not ok:
print(f"failed to get linear symbol sequence for {filename}")
return [""]
# tokens are incremented during graph construction
# so they need to be decremented
hyps = [id2token[i - 1] for i in osymbols_out]
# hyps = "".join(hyps).split("▁")
hyps = "".join(hyps).split("\u2581") # unicode codepoint of ▁
return hyps
@torch.no_grad()
def main():
parser = get_parser()
args = parser.parse_args()
device = torch.device("cpu")
logging.info(f"device: {device}")
logging.info("Loading torchscript model")
model = torch.jit.load(args.nn_model)
model.eval()
model.to(device)
logging.info(f"Loading H from {args.H}")
H = kaldifst.StdVectorFst.read(args.H)
sample_rate = 16000
logging.info("Constructing Fbank computer")
opts = kaldifeat.FbankOptions()
opts.device = device
opts.frame_opts.dither = 0
opts.frame_opts.snip_edges = False
opts.frame_opts.samp_freq = sample_rate
opts.mel_opts.num_bins = 80
fbank = kaldifeat.Fbank(opts)
logging.info(f"Reading sound files: {args.sound_files}")
waves = read_sound_files(
filenames=args.sound_files, expected_sample_rate=sample_rate
)
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
features = fbank(waves)
feature_lengths = [f.shape[0] for f in features]
feature_lengths = torch.tensor(feature_lengths)
supervisions = dict()
supervisions["sequence_idx"] = torch.arange(len(features))
supervisions["start_frame"] = torch.zeros(len(features))
supervisions["num_frames"] = feature_lengths
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
nnet_output, _, _ = model(features, supervisions)
feature_lengths = ((feature_lengths - 1) // 2 - 1) // 2
id2token = read_tokens(args.tokens)
hyps = []
for i in range(nnet_output.shape[0]):
hyp = decode(
filename=args.sound_files[i],
nnet_output=nnet_output[i, : feature_lengths[i]],
H=H,
id2token=id2token,
)
hyps.append(hyp)
s = "\n"
for filename, hyp in zip(args.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
logging.info(s)
logging.info("Decoding Done")
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
main()

1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_H.py Symbolic link
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../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_H.py

233
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py
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@ -1,233 +0,0 @@
#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
"""
This file shows how to use a torchscript model for decoding with HL
on CPU using OpenFST and decoders from kaldi.
Usage:
./conformer_ctc/jit_pretrained_decode_with_HL.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
--HL ./data/lang_char/HL.fst \
--words ./data/lang_char/words.txt \
./BAC009S0764W0121.wav \
./BAC009S0764W0122.wav \
./BAC009S0764W0123.wav
Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
you can use ./export.py --jit 1
"""
import argparse
import logging
import math
from typing import Dict, List
import kaldi_hmm_gmm
import kaldifeat
import kaldifst
import torch
import torchaudio
from kaldi_hmm_gmm import DecodableCtc, FasterDecoder, FasterDecoderOptions
from torch.nn.utils.rnn import pad_sequence
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--nn-model",
type=str,
required=True,
help="""Path to the torchscript model.
You can use ./conformer_ctc/export.py --jit 1
to obtain it
""",
)
parser.add_argument(
"--words",
type=str,
required=True,
help="Path to words.txt",
)
parser.add_argument("--HL", type=str, required=True, help="Path to HL.fst")
parser.add_argument(
"sound_files",
type=str,
nargs="+",
help="The input sound file(s) to transcribe. "
"Supported formats are those supported by torchaudio.load(). "
"For example, wav and flac are supported. ",
)
return parser
def read_words(words_txt: str) -> Dict[int, str]:
id2word = dict()
with open(words_txt, encoding="utf-8") as f:
for line in f:
word, idx = line.strip().split()
id2word[int(idx)] = word
return id2word
def read_sound_files(
filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
"""Read a list of sound files into a list 1-D float32 torch tensors.
Args:
filenames:
A list of sound filenames.
expected_sample_rate:
The expected sample rate of the sound files.
Returns:
Return a list of 1-D float32 torch tensors.
"""
ans = []
for f in filenames:
wave, sample_rate = torchaudio.load(f)
if sample_rate != expected_sample_rate:
wave = torchaudio.functional.resample(
wave,
orig_freq=sample_rate,
new_freq=expected_sample_rate,
)
# We use only the first channel
ans.append(wave[0].contiguous())
return ans
def decode(
filename: str,
nnet_output: torch.Tensor,
HL: kaldifst,
id2word: Dict[int, str],
) -> List[str]:
"""
Args:
filename:
Path to the filename for decoding. Used for debugging.
nnet_output:
A 2-D float32 tensor of shape (num_frames, vocab_size). It
contains output from log_softmax.
HL:
The HL graph.
word2token:
A map mapping token ID to word string.
Returns:
Return a list of decoded words.
"""
logging.info(f"{filename}, {nnet_output.shape}")
decodable = DecodableCtc(nnet_output.cpu())
decoder_opts = FasterDecoderOptions(max_active=3000)
decoder = FasterDecoder(HL, decoder_opts)
decoder.decode(decodable)
if not decoder.reached_final():
print(f"failed to decode {filename}")
return [""]
ok, best_path = decoder.get_best_path()
(
ok,
isymbols_out,
osymbols_out,
total_weight,
) = kaldifst.get_linear_symbol_sequence(best_path)
if not ok:
print(f"failed to get linear symbol sequence for {filename}")
return [""]
# are shifted by 1 during graph construction
hyps = [id2word[i] for i in osymbols_out]
return hyps
@torch.no_grad()
def main():
parser = get_parser()
args = parser.parse_args()
device = torch.device("cpu")
logging.info(f"device: {device}")
logging.info("Loading torchscript model")
model = torch.jit.load(args.nn_model)
model.eval()
model.to(device)
logging.info(f"Loading HL from {args.HL}")
HL = kaldifst.StdVectorFst.read(args.HL)
sample_rate = 16000
logging.info("Constructing Fbank computer")
opts = kaldifeat.FbankOptions()
opts.device = device
opts.frame_opts.dither = 0
opts.frame_opts.snip_edges = False
opts.frame_opts.samp_freq = sample_rate
opts.mel_opts.num_bins = 80
fbank = kaldifeat.Fbank(opts)
logging.info(f"Reading sound files: {args.sound_files}")
waves = read_sound_files(
filenames=args.sound_files, expected_sample_rate=sample_rate
)
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
features = fbank(waves)
feature_lengths = [f.shape[0] for f in features]
feature_lengths = torch.tensor(feature_lengths)
supervisions = dict()
supervisions["sequence_idx"] = torch.arange(len(features))
supervisions["start_frame"] = torch.zeros(len(features))
supervisions["num_frames"] = feature_lengths
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
nnet_output, _, _ = model(features, supervisions)
feature_lengths = ((feature_lengths - 1) // 2 - 1) // 2
id2word = read_words(args.words)
hyps = []
for i in range(nnet_output.shape[0]):
hyp = decode(
filename=args.sound_files[i],
nnet_output=nnet_output[i, : feature_lengths[i]],
HL=HL,
id2word=id2word,
)
hyps.append(hyp)
s = "\n"
for filename, hyp in zip(args.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
logging.info(s)
logging.info("Decoding Done")
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
main()

1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py Symbolic link
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@ -0,0 +1 @@
../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py

233
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py
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@ -1,233 +0,0 @@
#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
"""
This file shows how to use a torchscript model for decoding with HLG
on CPU using OpenFST and decoders from kaldi.
Usage:
./conformer_ctc/jit_pretrained_decode_with_HLG.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
--HLG ./data/lang_char/HLG.fst \
--words ./data/lang_char/words.txt \
./BAC009S0764W0121.wav \
./BAC009S0764W0122.wav \
./BAC009S0764W0123.wav
Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
you can use ./export.py --jit 1
"""
import argparse
import logging
import math
from typing import Dict, List
import kaldi_hmm_gmm
import kaldifeat
import kaldifst
import torch
import torchaudio
from kaldi_hmm_gmm import DecodableCtc, FasterDecoder, FasterDecoderOptions
from torch.nn.utils.rnn import pad_sequence
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--nn-model",
type=str,
required=True,
help="""Path to the torchscript model.
You can use ./conformer_ctc/export.py --jit 1
to obtain it
""",
)
parser.add_argument(
"--words",
type=str,
required=True,
help="Path to words.txt",
)
parser.add_argument("--HLG", type=str, required=True, help="Path to HLG.fst")
parser.add_argument(
"sound_files",
type=str,
nargs="+",
help="The input sound file(s) to transcribe. "
"Supported formats are those supported by torchaudio.load(). "
"For example, wav and flac are supported. ",
)
return parser
def read_words(words_txt: str) -> Dict[int, str]:
id2word = dict()
with open(words_txt, encoding="utf-8") as f:
for line in f:
word, idx = line.strip().split()
id2word[int(idx)] = word
return id2word
def read_sound_files(
filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
"""Read a list of sound files into a list 1-D float32 torch tensors.
Args:
filenames:
A list of sound filenames.
expected_sample_rate:
The expected sample rate of the sound files.
Returns:
Return a list of 1-D float32 torch tensors.
"""
ans = []
for f in filenames:
wave, sample_rate = torchaudio.load(f)
if sample_rate != expected_sample_rate:
wave = torchaudio.functional.resample(
wave,
orig_freq=sample_rate,
new_freq=expected_sample_rate,
)
# We use only the first channel
ans.append(wave[0].contiguous())
return ans
def decode(
filename: str,
nnet_output: torch.Tensor,
HLG: kaldifst,
id2word: Dict[int, str],
) -> List[str]:
"""
Args:
filename:
Path to the filename for decoding. Used for debugging.
nnet_output:
A 2-D float32 tensor of shape (num_frames, vocab_size). It
contains output from log_softmax.
HLG:
The HLG graph.
word2token:
A map mapping token ID to word string.
Returns:
Return a list of decoded words.
"""
logging.info(f"{filename}, {nnet_output.shape}")
decodable = DecodableCtc(nnet_output.cpu())
decoder_opts = FasterDecoderOptions(max_active=3000)
decoder = FasterDecoder(HLG, decoder_opts)
decoder.decode(decodable)
if not decoder.reached_final():
print(f"failed to decode {filename}")
return [""]
ok, best_path = decoder.get_best_path()
(
ok,
isymbols_out,
osymbols_out,
total_weight,
) = kaldifst.get_linear_symbol_sequence(best_path)
if not ok:
print(f"failed to get linear symbol sequence for {filename}")
return [""]
# are shifted by 1 during graph construction
hyps = [id2word[i] for i in osymbols_out]
return hyps
@torch.no_grad()
def main():
parser = get_parser()
args = parser.parse_args()
device = torch.device("cpu")
logging.info(f"device: {device}")
logging.info("Loading torchscript model")
model = torch.jit.load(args.nn_model)
model.eval()
model.to(device)
logging.info(f"Loading HLG from {args.HLG}")
HLG = kaldifst.StdVectorFst.read(args.HLG)
sample_rate = 16000
logging.info("Constructing Fbank computer")
opts = kaldifeat.FbankOptions()
opts.device = device
opts.frame_opts.dither = 0
opts.frame_opts.snip_edges = False
opts.frame_opts.samp_freq = sample_rate
opts.mel_opts.num_bins = 80
fbank = kaldifeat.Fbank(opts)
logging.info(f"Reading sound files: {args.sound_files}")
waves = read_sound_files(
filenames=args.sound_files, expected_sample_rate=sample_rate
)
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
features = fbank(waves)
feature_lengths = [f.shape[0] for f in features]
feature_lengths = torch.tensor(feature_lengths)
supervisions = dict()
supervisions["sequence_idx"] = torch.arange(len(features))
supervisions["start_frame"] = torch.zeros(len(features))
supervisions["num_frames"] = feature_lengths
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
nnet_output, _, _ = model(features, supervisions)
feature_lengths = ((feature_lengths - 1) // 2 - 1) // 2
id2word = read_words(args.words)
hyps = []
for i in range(nnet_output.shape[0]):
hyp = decode(
filename=args.sound_files[i],
nnet_output=nnet_output[i, : feature_lengths[i]],
HLG=HLG,
id2word=id2word,
)
hyps.append(hyp)
s = "\n"
for filename, hyp in zip(args.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
logging.info(s)
logging.info("Decoding Done")
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
main()

1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py Symbolic link
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@ -0,0 +1 @@
../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py

13
egs/librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_H.py
View File

@ -7,6 +7,8 @@ on CPU using OpenFST and decoders from kaldi.
Usage: Usage:
(1) LibriSpeech conformer_ctc
./conformer_ctc/jit_pretrained_decode_with_H.py \ ./conformer_ctc/jit_pretrained_decode_with_H.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \ --nn-model ./conformer_ctc/exp/cpu_jit.pt \
--H ./data/lang_bpe_500/H.fst \ --H ./data/lang_bpe_500/H.fst \
@ -14,6 +16,17 @@ Usage:
./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \ ./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \
./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac ./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac
(2) AIShell conformer_ctc
./conformer_ctc/jit_pretrained_decode_with_H.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
--H ./data/lang_char/H.fst \
--tokens ./data/lang_char/tokens.txt \
./BAC009S0764W0121.wav \
./BAC009S0764W0122.wav \
./BAC009S0764W0123.wav
Note that to generate ./conformer_ctc/exp/cpu_jit.pt, Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
you can use ./export.py --jit 1 you can use ./export.py --jit 1
""" """

13
egs/librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py
View File

@ -7,6 +7,8 @@ on CPU using OpenFST and decoders from kaldi.
Usage: Usage:
(1) LibriSpeech conformer_ctc
./conformer_ctc/jit_pretrained_decode_with_HL.py \ ./conformer_ctc/jit_pretrained_decode_with_HL.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \ --nn-model ./conformer_ctc/exp/cpu_jit.pt \
--HL ./data/lang_bpe_500/HL.fst \ --HL ./data/lang_bpe_500/HL.fst \
@ -14,6 +16,17 @@ Usage:
./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \ ./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \
./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac ./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac
(2) AIShell conformer_ctc
./conformer_ctc/jit_pretrained_decode_with_HL.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
--HL ./data/lang_char/HL.fst \
--words ./data/lang_char/words.txt \
./BAC009S0764W0121.wav \
./BAC009S0764W0122.wav \
./BAC009S0764W0123.wav
Note that to generate ./conformer_ctc/exp/cpu_jit.pt, Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
you can use ./export.py --jit 1 you can use ./export.py --jit 1
""" """

12
egs/librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py
View File

@ -7,6 +7,8 @@ on CPU using OpenFST and decoders from kaldi.
Usage: Usage:
(1) LibriSpeech conformer_ctc
./conformer_ctc/jit_pretrained_decode_with_HLG.py \ ./conformer_ctc/jit_pretrained_decode_with_HLG.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \ --nn-model ./conformer_ctc/exp/cpu_jit.pt \
--HLG ./data/lang_bpe_500/HLG.fst \ --HLG ./data/lang_bpe_500/HLG.fst \
@ -14,6 +16,16 @@ Usage:
./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \ ./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \
./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac ./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac
(2) AIShell conformer_ctc
./conformer_ctc/jit_pretrained_decode_with_HLG.py \
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
--HLG ./data/lang_char/HLG.fst \
--words ./data/lang_char/words.txt \
./BAC009S0764W0121.wav \
./BAC009S0764W0122.wav \
./BAC009S0764W0123.wav
Note that to generate ./conformer_ctc/exp/cpu_jit.pt, Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
you can use ./export.py --jit 1 you can use ./export.py --jit 1
""" """