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Remove duplicate files
This commit is contained in:
Fangjun Kuang
parent
1add6b0cdc
commit
205ec31068
@ -1,236 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
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"""
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This file shows how to use a torchscript model for decoding with H
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on CPU using OpenFST and decoders from kaldi.
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Usage:
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./conformer_ctc/jit_pretrained_decode_with_H.py \
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--nn-model ./conformer_ctc/exp/cpu_jit.pt \
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--H ./data/lang_char/H.fst \
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--tokens ./data/lang_char/tokens.txt \
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./BAC009S0764W0121.wav \
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./BAC009S0764W0122.wav \
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./BAC009S0764W0123.wav
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Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
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you can use ./export.py --jit 1
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"""
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import argparse
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import logging
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import math
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from typing import Dict, List
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import kaldi_hmm_gmm
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import kaldifeat
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import kaldifst
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import torch
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import torchaudio
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from kaldi_hmm_gmm import DecodableCtc, FasterDecoder, FasterDecoderOptions
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from torch.nn.utils.rnn import pad_sequence
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--nn-model",
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type=str,
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required=True,
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help="""Path to the torchscript model.
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You can use ./conformer_ctc/export.py --jit 1
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to obtain it
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""",
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)
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parser.add_argument(
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"--tokens",
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type=str,
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required=True,
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help="Path to tokens.txt",
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)
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parser.add_argument("--H", type=str, required=True, help="Path to H.fst")
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parser.add_argument(
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"sound_files",
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type=str,
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nargs="+",
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help="The input sound file(s) to transcribe. "
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"Supported formats are those supported by torchaudio.load(). "
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"For example, wav and flac are supported. ",
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)
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return parser
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def read_tokens(tokens_txt: str) -> Dict[int, str]:
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id2token = dict()
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with open(tokens_txt, encoding="utf-8") as f:
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for line in f:
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token, idx = line.strip().split()
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id2token[int(idx)] = token
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return id2token
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def read_sound_files(
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filenames: List[str], expected_sample_rate: float
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) -> List[torch.Tensor]:
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"""Read a list of sound files into a list 1-D float32 torch tensors.
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Args:
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filenames:
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A list of sound filenames.
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expected_sample_rate:
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The expected sample rate of the sound files.
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Returns:
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Return a list of 1-D float32 torch tensors.
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"""
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ans = []
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for f in filenames:
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wave, sample_rate = torchaudio.load(f)
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if sample_rate != expected_sample_rate:
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wave = torchaudio.functional.resample(
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wave,
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orig_freq=sample_rate,
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new_freq=expected_sample_rate,
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)
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# We use only the first channel
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ans.append(wave[0].contiguous())
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return ans
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def decode(
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filename: str,
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nnet_output: torch.Tensor,
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H: kaldifst,
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id2token: Dict[int, str],
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) -> List[str]:
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"""
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Args:
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filename:
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Path to the filename for decoding. Used for debugging.
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nnet_output:
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A 2-D float32 tensor of shape (num_frames, vocab_size). It
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contains output from log_softmax.
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H:
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The H graph.
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id2token:
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A map mapping token ID to token string.
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Returns:
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Return a list of decoded tokens.
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"""
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logging.info(f"{filename}, {nnet_output.shape}")
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decodable = DecodableCtc(nnet_output.cpu())
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decoder_opts = FasterDecoderOptions(max_active=3000)
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decoder = FasterDecoder(H, decoder_opts)
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decoder.decode(decodable)
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if not decoder.reached_final():
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print(f"failed to decode {filename}")
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return [""]
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ok, best_path = decoder.get_best_path()
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(
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ok,
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isymbols_out,
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osymbols_out,
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total_weight,
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) = kaldifst.get_linear_symbol_sequence(best_path)
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if not ok:
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print(f"failed to get linear symbol sequence for {filename}")
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return [""]
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# tokens are incremented during graph construction
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# so they need to be decremented
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hyps = [id2token[i - 1] for i in osymbols_out]
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# hyps = "".join(hyps).split("▁")
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hyps = "".join(hyps).split("\u2581") # unicode codepoint of ▁
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return hyps
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@torch.no_grad()
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def main():
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parser = get_parser()
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args = parser.parse_args()
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device = torch.device("cpu")
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logging.info(f"device: {device}")
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logging.info("Loading torchscript model")
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model = torch.jit.load(args.nn_model)
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model.eval()
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model.to(device)
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logging.info(f"Loading H from {args.H}")
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H = kaldifst.StdVectorFst.read(args.H)
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sample_rate = 16000
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logging.info("Constructing Fbank computer")
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opts = kaldifeat.FbankOptions()
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opts.device = device
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opts.frame_opts.dither = 0
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opts.frame_opts.snip_edges = False
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opts.frame_opts.samp_freq = sample_rate
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opts.mel_opts.num_bins = 80
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fbank = kaldifeat.Fbank(opts)
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logging.info(f"Reading sound files: {args.sound_files}")
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waves = read_sound_files(
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filenames=args.sound_files, expected_sample_rate=sample_rate
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)
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waves = [w.to(device) for w in waves]
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logging.info("Decoding started")
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features = fbank(waves)
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feature_lengths = [f.shape[0] for f in features]
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feature_lengths = torch.tensor(feature_lengths)
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supervisions = dict()
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supervisions["sequence_idx"] = torch.arange(len(features))
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supervisions["start_frame"] = torch.zeros(len(features))
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supervisions["num_frames"] = feature_lengths
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features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
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nnet_output, _, _ = model(features, supervisions)
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feature_lengths = ((feature_lengths - 1) // 2 - 1) // 2
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id2token = read_tokens(args.tokens)
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hyps = []
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for i in range(nnet_output.shape[0]):
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hyp = decode(
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filename=args.sound_files[i],
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nnet_output=nnet_output[i, : feature_lengths[i]],
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H=H,
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id2token=id2token,
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)
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hyps.append(hyp)
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s = "\n"
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for filename, hyp in zip(args.sound_files, hyps):
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words = " ".join(hyp)
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s += f"{filename}:\n{words}\n\n"
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logging.info(s)
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logging.info("Decoding Done")
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if __name__ == "__main__":
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_H.py
Symbolic link
1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_H.py
Symbolic link
@ -0,0 +1 @@
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../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_H.py
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@ -1,233 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
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"""
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This file shows how to use a torchscript model for decoding with HL
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on CPU using OpenFST and decoders from kaldi.
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Usage:
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./conformer_ctc/jit_pretrained_decode_with_HL.py \
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--nn-model ./conformer_ctc/exp/cpu_jit.pt \
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--HL ./data/lang_char/HL.fst \
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--words ./data/lang_char/words.txt \
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./BAC009S0764W0121.wav \
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./BAC009S0764W0122.wav \
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./BAC009S0764W0123.wav
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Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
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you can use ./export.py --jit 1
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"""
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import argparse
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import logging
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import math
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from typing import Dict, List
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import kaldi_hmm_gmm
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import kaldifeat
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import kaldifst
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import torch
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import torchaudio
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from kaldi_hmm_gmm import DecodableCtc, FasterDecoder, FasterDecoderOptions
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from torch.nn.utils.rnn import pad_sequence
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--nn-model",
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type=str,
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required=True,
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help="""Path to the torchscript model.
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You can use ./conformer_ctc/export.py --jit 1
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to obtain it
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""",
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)
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parser.add_argument(
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"--words",
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type=str,
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required=True,
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help="Path to words.txt",
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)
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parser.add_argument("--HL", type=str, required=True, help="Path to HL.fst")
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parser.add_argument(
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"sound_files",
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type=str,
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nargs="+",
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help="The input sound file(s) to transcribe. "
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"Supported formats are those supported by torchaudio.load(). "
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"For example, wav and flac are supported. ",
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)
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return parser
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def read_words(words_txt: str) -> Dict[int, str]:
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id2word = dict()
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with open(words_txt, encoding="utf-8") as f:
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for line in f:
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word, idx = line.strip().split()
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id2word[int(idx)] = word
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return id2word
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def read_sound_files(
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filenames: List[str], expected_sample_rate: float
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) -> List[torch.Tensor]:
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"""Read a list of sound files into a list 1-D float32 torch tensors.
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Args:
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filenames:
|
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A list of sound filenames.
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expected_sample_rate:
|
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The expected sample rate of the sound files.
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Returns:
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Return a list of 1-D float32 torch tensors.
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"""
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ans = []
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for f in filenames:
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wave, sample_rate = torchaudio.load(f)
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if sample_rate != expected_sample_rate:
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wave = torchaudio.functional.resample(
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wave,
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orig_freq=sample_rate,
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new_freq=expected_sample_rate,
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)
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# We use only the first channel
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ans.append(wave[0].contiguous())
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return ans
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def decode(
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filename: str,
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nnet_output: torch.Tensor,
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HL: kaldifst,
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id2word: Dict[int, str],
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) -> List[str]:
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"""
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Args:
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filename:
|
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Path to the filename for decoding. Used for debugging.
|
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nnet_output:
|
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A 2-D float32 tensor of shape (num_frames, vocab_size). It
|
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contains output from log_softmax.
|
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HL:
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The HL graph.
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word2token:
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A map mapping token ID to word string.
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Returns:
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Return a list of decoded words.
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"""
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logging.info(f"{filename}, {nnet_output.shape}")
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decodable = DecodableCtc(nnet_output.cpu())
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decoder_opts = FasterDecoderOptions(max_active=3000)
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decoder = FasterDecoder(HL, decoder_opts)
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decoder.decode(decodable)
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if not decoder.reached_final():
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print(f"failed to decode {filename}")
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return [""]
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ok, best_path = decoder.get_best_path()
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(
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ok,
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isymbols_out,
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osymbols_out,
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total_weight,
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) = kaldifst.get_linear_symbol_sequence(best_path)
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if not ok:
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print(f"failed to get linear symbol sequence for {filename}")
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return [""]
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# are shifted by 1 during graph construction
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hyps = [id2word[i] for i in osymbols_out]
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return hyps
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@torch.no_grad()
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def main():
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parser = get_parser()
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args = parser.parse_args()
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device = torch.device("cpu")
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logging.info(f"device: {device}")
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logging.info("Loading torchscript model")
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model = torch.jit.load(args.nn_model)
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model.eval()
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model.to(device)
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logging.info(f"Loading HL from {args.HL}")
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HL = kaldifst.StdVectorFst.read(args.HL)
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sample_rate = 16000
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logging.info("Constructing Fbank computer")
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opts = kaldifeat.FbankOptions()
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opts.device = device
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opts.frame_opts.dither = 0
|
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opts.frame_opts.snip_edges = False
|
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opts.frame_opts.samp_freq = sample_rate
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opts.mel_opts.num_bins = 80
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|
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fbank = kaldifeat.Fbank(opts)
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|
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logging.info(f"Reading sound files: {args.sound_files}")
|
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waves = read_sound_files(
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filenames=args.sound_files, expected_sample_rate=sample_rate
|
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)
|
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waves = [w.to(device) for w in waves]
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|
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logging.info("Decoding started")
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features = fbank(waves)
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feature_lengths = [f.shape[0] for f in features]
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feature_lengths = torch.tensor(feature_lengths)
|
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|
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supervisions = dict()
|
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supervisions["sequence_idx"] = torch.arange(len(features))
|
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supervisions["start_frame"] = torch.zeros(len(features))
|
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supervisions["num_frames"] = feature_lengths
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|
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features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
|
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|
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nnet_output, _, _ = model(features, supervisions)
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feature_lengths = ((feature_lengths - 1) // 2 - 1) // 2
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|
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id2word = read_words(args.words)
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|
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hyps = []
|
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for i in range(nnet_output.shape[0]):
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hyp = decode(
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filename=args.sound_files[i],
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nnet_output=nnet_output[i, : feature_lengths[i]],
|
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HL=HL,
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id2word=id2word,
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)
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hyps.append(hyp)
|
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|
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s = "\n"
|
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for filename, hyp in zip(args.sound_files, hyps):
|
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words = " ".join(hyp)
|
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s += f"{filename}:\n{words}\n\n"
|
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logging.info(s)
|
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|
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logging.info("Decoding Done")
|
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|
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|
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if __name__ == "__main__":
|
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
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main()
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1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py
Symbolic link
1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py
|
||||
@ -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
1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py
|
||||
@ -7,6 +7,8 @@ on CPU using OpenFST and decoders from kaldi.
|
||||
|
||||
Usage:
|
||||
|
||||
(1) LibriSpeech conformer_ctc
|
||||
|
||||
./conformer_ctc/jit_pretrained_decode_with_H.py \
|
||||
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
|
||||
--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/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,
|
||||
you can use ./export.py --jit 1
|
||||
"""
|
||||
|
||||
@ -7,6 +7,8 @@ on CPU using OpenFST and decoders from kaldi.
|
||||
|
||||
Usage:
|
||||
|
||||
(1) LibriSpeech conformer_ctc
|
||||
|
||||
./conformer_ctc/jit_pretrained_decode_with_HL.py \
|
||||
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
|
||||
--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/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,
|
||||
you can use ./export.py --jit 1
|
||||
"""
|
||||
|
||||
@ -7,6 +7,8 @@ on CPU using OpenFST and decoders from kaldi.
|
||||
|
||||
Usage:
|
||||
|
||||
(1) LibriSpeech conformer_ctc
|
||||
|
||||
./conformer_ctc/jit_pretrained_decode_with_HLG.py \
|
||||
--nn-model ./conformer_ctc/exp/cpu_jit.pt \
|
||||
--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/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,
|
||||
you can use ./export.py --jit 1
|
||||
"""
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user