Add decoding with H and HL for LibriSpeech

.github/scripts/run-pre-trained-conformer-ctc.sh

@@ -44,3 +44,46 @@ log "HLG decoding"
   $repo/test_wavs/1089-134686-0001.flac \
   $repo/test_wavs/1221-135766-0001.flac \
   $repo/test_wavs/1221-135766-0002.flac
+
+log "CTC decoding on CPU with kaldi decoders using OpenFst"
+
+log "Exporting model with torchscript"
+
+pushd $repo/exp
+ln -s pretrained.pt epoch-99.pt
+popd
+
+./conformer_ctc/export.py \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --jit 1
+
+ls -lh $repo/exp
+
+
+log "Generating H.fst, HL.fst"
+
+./local/prepare_lang_fst.py  --lang-dir $repo/data/lang_bpe_500
+ls -lh $repo/data/lang_bpe_500
+
+log "Decoding with H on CPU with OpenFst"
+
+./conformer_ctc/jit_pretrained_decode_with_H.py \
+  --nn-model $repo/exp/cpu_jit.pt \
+  --H $repo/data/lang_bpe_500/H.fst \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  $repo/test_wavs/1089-134686-0001.flac \
+  $repo/test_wavs/1221-135766-0001.flac \
+  $repo/test_wavs/1221-135766-0002.flac
+
+log "Decoding with HL on CPU with OpenFst"
+
+./conformer_ctc/jit_pretrained_decode_with_H.py \
+  --nn-model $repo/exp/cpu_jit.pt \
+  --HL $repo/data/lang_bpe_500/HL.fst \
+  --words $repo/data/lang_bpe_500/words.txt \
+  $repo/test_wavs/1089-134686-0001.flac \
+  $repo/test_wavs/1221-135766-0001.flac \
+  $repo/test_wavs/1221-135766-0002.flac

.github/workflows/run-pretrained-conformer-ctc.yml

@@ -29,7 +29,7 @@ concurrency:
 
 jobs:
   run_pre_trained_conformer_ctc:
-    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'ctc'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:

egs/librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_H.py

@@ -0,0 +1,221 @@
+#!/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 ./cpu_jit.pt \
+      --H ./data/lang_bpe_500/H.fst \
+      --tokens ./data/lang_bpe_500/tokens.txt \
+      ./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \
+      ./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac
+
+Note that to generate ./tdnn/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 ./tdnn/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]:
+    logging.info(f"{filename}, {nnet_output.shape}")
+    decodable = DecodableCtc(nnet_output)
+
+    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()