mirror of
https://github.com/k2-fsa/icefall.git
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0622dea30d
* first commit * replace phonimizer with g2p * use Conformer as text encoder * modify training script, clean codes * rename directory * convert text to tokens in data preparation stage * fix tts_datamodule.py * support onnx export and testing the exported onnx model * add doc * add README.md * fix style
107 lines
3.4 KiB
Python
Executable File
107 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang,
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# Zengwei Yao)
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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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"""
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This file computes fbank features of the LJSpeech dataset.
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It looks for manifests in the directory data/manifests.
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The generated spectrogram features are saved in data/spectrogram.
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"""
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import logging
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import os
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from pathlib import Path
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import torch
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from lhotse import (
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CutSet,
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LilcomChunkyWriter,
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Spectrogram,
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SpectrogramConfig,
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load_manifest,
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)
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from lhotse.audio import RecordingSet
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from lhotse.supervision import SupervisionSet
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from icefall.utils import get_executor
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# Torch's multithreaded behavior needs to be disabled or
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# it wastes a lot of CPU and slow things down.
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# Do this outside of main() in case it needs to take effect
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# even when we are not invoking the main (e.g. when spawning subprocesses).
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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def compute_spectrogram_ljspeech():
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src_dir = Path("data/manifests")
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output_dir = Path("data/spectrogram")
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num_jobs = min(4, os.cpu_count())
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sampling_rate = 22050
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frame_length = 1024 / sampling_rate # (in second)
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frame_shift = 256 / sampling_rate # (in second)
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use_fft_mag = True
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prefix = "ljspeech"
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suffix = "jsonl.gz"
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partition = "all"
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recordings = load_manifest(
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src_dir / f"{prefix}_recordings_{partition}.{suffix}", RecordingSet
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)
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supervisions = load_manifest(
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src_dir / f"{prefix}_supervisions_{partition}.{suffix}", SupervisionSet
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)
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config = SpectrogramConfig(
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sampling_rate=sampling_rate,
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frame_length=frame_length,
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frame_shift=frame_shift,
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use_fft_mag=use_fft_mag,
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)
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extractor = Spectrogram(config)
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with get_executor() as ex: # Initialize the executor only once.
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cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
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if (output_dir / cuts_filename).is_file():
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logging.info(f"{cuts_filename} already exists - skipping.")
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return
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logging.info(f"Processing {partition}")
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cut_set = CutSet.from_manifests(
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recordings=recordings, supervisions=supervisions
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)
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cut_set = cut_set.compute_and_store_features(
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extractor=extractor,
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storage_path=f"{output_dir}/{prefix}_feats_{partition}",
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# when an executor is specified, make more partitions
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num_jobs=num_jobs if ex is None else 80,
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executor=ex,
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storage_type=LilcomChunkyWriter,
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)
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cut_set.to_file(output_dir / cuts_filename)
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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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compute_spectrogram_ljspeech()
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