pretrain scriptping

.gitignore

@@ -11,3 +11,5 @@ log
 *.bak
 *-bak
 *bak.py
+birch
+.vscode

egs/gigaspeech/ASR/conformer_ctc/decode.py

@@ -660,7 +660,10 @@ def main():
     )
 
     if params.avg == 1:
-        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        if params.epoch == 0:
+            load_checkpoint(f"{params.exp_dir}/pretrained.pt", model)
+        else:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
     else:
         start = params.epoch - params.avg + 1
         filenames = []
@@ -684,8 +687,8 @@ def main():
     dev_dl = gigaspeech.test_dataloaders(dev_cuts)
     test_dl = gigaspeech.test_dataloaders(test_cuts)
 
-    test_sets = ["dev", "test"]
+    test_sets = ["test"]
-    test_dls = [dev_dl, test_dl]
+    test_dls = [test_dl]
 
     for test_set, test_dl in zip(test_sets, test_dls):
         results_dict = decode_dataset(

egs/gigaspeech/ASR/conformer_ctc/pretrained.py

@@ -0,0 +1,533 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+from genericpath import exists
+import logging
+import math
+from typing import Dict, List, Optional, Tuple
+
+
+import k2
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torchaudio
+import torch.nn as nn
+from shortuuid import ShortUUID
+from pathlib import Path
+import os
+import datetime
+from torch.utils.data import Dataset, DataLoader
+
+from conformer import Conformer
+from torch.nn.utils.rnn import pad_sequence
+
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_attention_decoder,
+    rescore_with_whole_lattice,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import AttributeDict, get_texts
+import string
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        help="""Path to words.txt.
+        Used only when method is not ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--HLG",
+        type=str,
+        help="""Path to HLG.pt.
+        Used only when method is not ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        help="""Path to bpe.model.
+        Used only when method is ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (0) ctc-decoding - Use CTC decoding. It uses a sentence
+            piece model, i.e., lang_dir/bpe.model, to convert
+            word pieces to words. It needs neither a lexicon
+            nor an n-gram LM.
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        (3) attention-decoder - Extract n paths from the rescored
+            lattice and use the transformer attention decoder for
+            rescoring.
+            We call it HLG decoding + n-gram LM rescoring + attention
+            decoder rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring or attention-decoder.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=100,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the size of n-best list.""",
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.2,
+        help="""
+        Used only when method is whole-lattice-rescoring and attention-decoder.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--attention-decoder-scale",
+        type=float,
+        default=1.2,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the scale for attention decoder scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the scale for lattice.scores when
+        extracting n-best lists. A smaller value results in
+        more unique number of paths with the risk of missing
+        the best path.
+        """,
+    )
+
+    parser.add_argument(
+        "--sos-id",
+        type=int,
+        default=1,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies ID for the SOS token.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-classes",
+        type=int,
+        default=500,
+        help="""
+        Vocab size in the BPE model.
+        """,
+    )
+
+    parser.add_argument(
+        "--eos-id",
+        type=int,
+        default=1,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies ID for the EOS token.
+        """,
+    )
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="""
+        sampling rate for input sound files
+        """,
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="output",
+        help="""
+        Output directory name, we store output hypothesis 
+        """
+
+    )
+
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=10,
+        help="""
+        Number of input files in one batch, defaulted to 10
+        """
+    )
+    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. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "sample_rate": 16000,
+            # parameters for conformer
+            "subsampling_factor": 4,
+            "vgg_frontend": False,
+            "use_feat_batchnorm": True,
+            "feature_dim": 80,
+            "nhead": 8,
+            "attention_dim": 512,
+            "num_decoder_layers": 6,
+            # parameters for decoding
+            "search_beam": 20,  # default 20
+            "output_beam": 8,  # default 8
+            "min_active_states": 30,
+            "max_active_states": 3000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+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 = []
+    wave_names = []
+
+    def loadfile(filename):
+        wave, sample_rate = torchaudio.load(filename)
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+        wave_names.append(str(filename))
+
+    for f in filenames:
+        file_path = Path(f)
+        if file_path.is_file():
+            loadfile(file_path)
+        elif file_path.is_dir():
+            for filename in file_path.iterdir():
+                loadfile(filename)
+        else:
+            logging.error(f"{f} must be a filename or a dirname")
+    return ans, wave_names
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    decoding_graph: Optional[k2.Fsa],
+    feature: torch.Tensor,
+    bpe_model: Optional[spm.SentencePieceProcessor] = None,
+    G: Optional[k2.Fsa] = None,
+) -> Dict[str, List[List[str]]]:
+
+    device = decoding_graph.device
+    assert feature.ndim == 3
+    feature = feature.to(device)
+    with torch.no_grad():
+        nnet_output, memory, memory_key_padding_mask = model(feature)
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        decoding_graph=decoding_graph,
+        supervision_segments=supervision_segments,
+        search_beam=params.search_beam,
+        output_beam=params.output_beam,
+        min_active_states=params.min_active_states,
+        max_active_states=params.max_active_states,
+        subsampling_factor=params.subsampling_factor,
+    )
+
+    if params.method == "ctc-decoding":
+
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+        token_ids = get_texts(best_path)
+        hyps = bpe_model.decode(token_ids)
+        hyps = [s.split() for s in hyps]
+
+    elif params.method in [
+        "1best",
+        "whole-lattice-rescoring",
+        "attention-decoder",
+    ]:
+
+        if params.method == "1best":
+            logging.info("Use HLG decoding")
+            best_path = one_best_decoding(
+                lattice=lattice, use_double_scores=params.use_double_scores
+            )
+        elif params.method == "whole-lattice-rescoring":
+            logging.info("Use HLG decoding + LM rescoring")
+            best_path_dict = rescore_with_whole_lattice(
+                lattice=lattice,
+                G_with_epsilon_loops=G,
+                lm_scale_list=[params.ngram_lm_scale],
+            )
+            best_path = next(iter(best_path_dict.values()))
+        elif params.method == "attention-decoder":
+            logging.info("Use HLG + LM rescoring + attention decoder rescoring")
+            rescored_lattice = rescore_with_whole_lattice(
+                lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None
+            )
+            best_path_dict = rescore_with_attention_decoder(
+                lattice=rescored_lattice,
+                num_paths=params.num_paths,
+                model=model,
+                memory=memory,
+                memory_key_padding_mask=memory_key_padding_mask,
+                sos_id=params.sos_id,
+                eos_id=params.eos_id,
+                nbest_scale=params.nbest_scale,
+                ngram_lm_scale=params.ngram_lm_scale,
+                attention_scale=params.attention_decoder_scale,
+            )
+            best_path = next(iter(best_path_dict.values()))
+
+        hyps = get_texts(best_path)
+        word_sym_table = k2.SymbolTable.from_file(params.words_file)
+        hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+
+    return hyps
+
+
+class TestDataset(Dataset):
+    def __init__(self, features: torch.Tensor):
+        self.features = features
+
+    def __len__(self):
+        return len(self.features)
+
+    def __getitem__(self, idx):
+        return (self.features[idx], 0)
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    if args.method != "attention-decoder":
+        # to save memory as the attention decoder
+        # will not be used
+        params.num_decoder_layers = 0
+
+    params.update(vars(args))
+
+    su = ShortUUID(alphabet=string.ascii_lowercase + string.digits)
+    output_dir = Path(args.output_dir)/(su.random(length=5) + '-' + datetime.datetime.now().strftime(
+        "%Y-%m-%d_%H-%M"))
+    output_dir.mkdir(exist_ok=True, parents=True)
+
+    with (output_dir/"command").open("w") as f_cmd:
+        f_cmd.write(f"{args}\n")
+        f_cmd.write(f"{params}\n")
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = Conformer(
+        num_features=params.feature_dim,
+        nhead=params.nhead,
+        d_model=params.attention_dim,
+        num_classes=params.num_classes,
+        subsampling_factor=params.subsampling_factor,
+        num_decoder_layers=params.num_decoder_layers,
+        vgg_frontend=params.vgg_frontend,
+        use_feat_batchnorm=params.use_feat_batchnorm,
+    )
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+
+    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 = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves, wave_names = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
+
+    G, bpe_model = None, None
+    if params.method == "ctc-decoding":
+        logging.info("Use CTC decoding")
+        bpe_model = spm.SentencePieceProcessor()
+        bpe_model.load(params.bpe_model)
+        max_token_id = params.num_classes - 1
+
+        H = k2.ctc_topo(
+            max_token=max_token_id,
+            modified=False,
+            device=device,
+        )
+        H.to(device)
+        decoding_graph = H
+    elif params.method in [
+        "1best",
+        "whole-lattice-rescoring",
+        "attention-decoder",
+    ]:
+        logging.info(f"Loading HLG from {params.HLG}")
+        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = HLG.to(device)
+        decoding_graph = HLG
+        if not hasattr(HLG, "lm_scores"):
+            # For whole-lattice-rescoring and attention-decoder
+            HLG.lm_scores = HLG.scores.clone()
+
+        if params.method in [
+            "whole-lattice-rescoring",
+            "attention-decoder",
+        ]:
+            logging.info(f"Loading G from {params.G}")
+            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            # Add epsilon self-loops to G as we will compose
+            # it with the whole lattice later
+            G = G.to(device)
+            G = k2.add_epsilon_self_loops(G)
+            G = k2.arc_sort(G)
+            G.lm_scores = G.scores.clone()
+
+    else:
+        raise ValueError(f"Unsupported decoding method: {params.method}")
+
+    testdata = TestDataset(features)
+    tl = DataLoader(testdata, batch_size=args.batch_size)
+    hyps = []
+    num_batches = len(tl)
+
+    for batch_idx, batch in enumerate(tl):
+        hyps.extend(decode_one_batch(
+            params, model, decoding_graph, batch[0], bpe_model, G))
+        logging.info(
+            f"batch {batch_idx + 1}/{num_batches}, cuts processed until now is {len(hyps)}")
+
+    logging.info(f"Writing hypothesis to output dir {output_dir}")
+    s = "\n"
+    for filename, hyp in zip(wave_names, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+        with (output_dir/os.path.basename(filename.replace(".wav", ".txt"))).open("w") as f_hyp:
+            f_hyp.write(words)
+    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()

env.sh

@@ -0,0 +1,4 @@
+export PYTHONPATH=$PYTHONPATH:/usr/local/cuda-10.1/targets/x86_64-linux/lib/
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.1/targets/x86_64-linux/lib/
+export PATH=$PATH:/usr/local/cuda-10.1/bin/
+export CUDA_HOME=/usr/local/cuda-10.1/