Remove ncnn as it is not fully tested yet.

egs/librispeech/ASR/pruned_transducer_stateless3/ncnn_pretrained.py

@@ -1,161 +0,0 @@
-#!/usr/bin/env python3
-# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# 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.
-"""
-This script loads ncnn models and uses them to decode waves.
-
-./pruned_transducer_stateless3/jit_pretrained.py \
-  --model-dir /path/to/ncnn/model_dir
-  --bpe-model ./data/lang_bpe_500/bpe.model \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-
-We assume there exist following files in the given `model_dir`:
-
-    - encoder_jit_trace.ncnn.param
-    - encoder_jit_trace.ncnn.bin
-    - decoder_jit_trace.ncnn.param
-    - decoder_jit_trace.ncnn.bin
-    - joiner_jit_trace.ncnn.param
-    - joiner_jit_trace.ncnn.bin
-"""
-
-import argparse
-import logging
-from pathlib import Path
-from typing import List
-
-import ncnn
-import torch
-import torchaudio
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--model-dir",
-        type=str,
-        required=True,
-        help="Path to the ncnn models directory. ",
-    )
-
-    parser.add_argument(
-        "--bpe-model",
-        type=str,
-        help="""Path to bpe.model.""",
-    )
-
-    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.",
-    )
-
-    parser.add_argument(
-        "--sample-rate",
-        type=int,
-        default=16000,
-        help="The sample rate of the input sound file",
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=2,
-        help="Context size of the decoder model",
-    )
-
-    return parser
-
-
-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)
-        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])
-    return ans
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    logging.info(vars(args))
-
-    model_dir = Path(args.model_dir)
-    encoder_param = model_dir / "encoder_jit_trace.ncnn.param"
-    encoder_bin = model_dir / "encoder_jit_trace.ncnn.bin"
-
-    decoder_param = model_dir / "decoder_jit_trace.ncnn.param"
-    decoder_bin = model_dir / "decoder_jit_trace.ncnn.bin"
-
-    joiner_param = model_dir / "joiner_jit_trace.ncnn.param"
-    joiner_bin = model_dir / "joiner_jit_trace.ncnn.bin"
-
-    assert encoder_param.is_file()
-    assert encoder_bin.is_file()
-
-    assert decoder_param.is_file()
-    assert decoder_bin.is_file()
-
-    assert joiner_param.is_file()
-    assert joiner_bin.is_file()
-
-    encoder = ncnn.Net()
-    decoder = ncnn.Net()
-    joiner = ncnn.Net()
-
-    #  encoder.load_param(str(encoder_param)) # not working yet
-    #  decoder.load_param(str(decoder_param))
-    joiner.load_param(str(joiner_param))
-
-    encoder.clear()
-    decoder.clear()
-    joiner.clear()
-
-    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()