2e0f255ada
* add timit recipe for icefall * add shared file * update the docs for timit recipe * Delete shared * update the timit recipe and check style * Update model.py * Do some changes * Update model.py * Update model.py * Add README.md and RESULTS.md * Update RESULTS.md * Update README.md * update the docs for timit recipe
279 lines
8.0 KiB
Python
279 lines
8.0 KiB
Python
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang,
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# Wei Kang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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 List
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import k2
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import kaldifeat
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import torch
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import torchaudio
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from model import TdnnLiGRU
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from torch.nn.utils.rnn import pad_sequence
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from icefall.decode import (
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get_lattice,
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one_best_decoding,
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rescore_with_whole_lattice,
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)
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from icefall.utils import AttributeDict, get_env_info, get_texts
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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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"--checkpoint",
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type=str,
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required=True,
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help="Path to the checkpoint. "
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"The checkpoint is assumed to be saved by "
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"icefall.checkpoint.save_checkpoint().",
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)
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parser.add_argument(
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"--words-file",
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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(
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"--HLG", type=str, required=True, help="Path to HLG.pt."
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)
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parser.add_argument(
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"--method",
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type=str,
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default="1best",
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help="""Decoding method.
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Possible values are:
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(1) 1best - Use the best path as decoding output. Only
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the transformer encoder output is used for decoding.
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We call it HLG decoding.
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(2) whole-lattice-rescoring - Use an LM to rescore the
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decoding lattice and then use 1best to decode the
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rescored lattice.
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We call it HLG decoding + n-gram LM rescoring.
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""",
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)
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parser.add_argument(
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"--G",
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type=str,
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help="""An LM for rescoring.
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Used only when method is
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whole-lattice-rescoring.
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It's usually a 4-gram LM.
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""",
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)
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parser.add_argument(
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"--ngram-lm-scale",
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type=float,
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default=0.1,
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help="""
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Used only when method is whole-lattice-rescoring.
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It specifies the scale for n-gram LM scores.
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(Note: You need to tune it on a dataset.)
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""",
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)
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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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"The sample rate has to be 16kHz.",
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)
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return parser
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def get_params() -> AttributeDict:
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params = AttributeDict(
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{
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"feature_dim": 80,
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"subsampling_factor": 2,
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"num_classes": 41,
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"sample_rate": 16000,
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"search_beam": 20,
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"output_beam": 5,
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"min_active_states": 30,
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"max_active_states": 10000,
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"use_double_scores": True,
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}
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)
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return params
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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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assert sample_rate == expected_sample_rate, (
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f"expected sample rate: {expected_sample_rate}. "
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f"Given: {sample_rate}"
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)
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# We use only the first channel
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ans.append(wave[0])
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return ans
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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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params = get_params()
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params.update(vars(args))
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params["env_info"] = get_env_info()
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logging.info(f"{params}")
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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logging.info(f"device: {device}")
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logging.info("Creating model")
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model = TdnnLiGRU(
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num_features=params.feature_dim,
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num_classes=params.num_classes,
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subsampling_factor=params.subsampling_factor,
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)
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checkpoint = torch.load(args.checkpoint, map_location="cpu")
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model.load_state_dict(checkpoint["model"])
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model.to(device)
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model.eval()
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logging.info(f"Loading HLG from {params.HLG}")
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HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
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HLG = HLG.to(device)
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if not hasattr(HLG, "lm_scores"):
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# For whole-lattice-rescoring and attention-decoder
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HLG.lm_scores = HLG.scores.clone()
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if params.method == "whole-lattice-rescoring":
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logging.info(f"Loading G from {params.G}")
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G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
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# Add epsilon self-loops to G as we will compose
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# it with the whole lattice later
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G = G.to(device)
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G = k2.add_epsilon_self_loops(G)
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G = k2.arc_sort(G)
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G.lm_scores = G.scores.clone()
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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 = params.sample_rate
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opts.mel_opts.num_bins = params.feature_dim
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fbank = kaldifeat.Fbank(opts)
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logging.info(f"Reading sound files: {params.sound_files}")
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waves = read_sound_files(
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filenames=params.sound_files, expected_sample_rate=params.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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features = pad_sequence(
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features, batch_first=True, padding_value=math.log(1e-10)
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)
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features = features.permute(0, 2, 1) # now features is (N, C, T)
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with torch.no_grad():
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nnet_output = model(features)
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# nnet_output is (N, T, C)
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batch_size = nnet_output.shape[0]
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supervision_segments = torch.tensor(
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[[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
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dtype=torch.int32,
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)
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=HLG,
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supervision_segments=supervision_segments,
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search_beam=params.search_beam,
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output_beam=params.output_beam,
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min_active_states=params.min_active_states,
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max_active_states=params.max_active_states,
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subsampling_factor=params.subsampling_factor,
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)
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if params.method == "1best":
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logging.info("Use HLG decoding")
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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elif params.method == "whole-lattice-rescoring":
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logging.info("Use HLG decoding + LM rescoring")
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best_path_dict = rescore_with_whole_lattice(
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lattice=lattice,
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G_with_epsilon_loops=G,
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lm_scale_list=[params.ngram_lm_scale],
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)
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best_path = next(iter(best_path_dict.values()))
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hyps = get_texts(best_path)
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word_sym_table = k2.SymbolTable.from_file(params.words_file)
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hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
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s = "\n"
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for filename, hyp in zip(params.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 = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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