4771c9275c
* Add lm decode for the Python API. * fix style. * Fix LogAdd, Shouldn't double lm_log_prob when merge same prefix path * sort the import alphabetically
306 lines
9.0 KiB
Python
Executable File
306 lines
9.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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This file demonstrates how to use sherpa-onnx Python API to transcribe
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file(s) with a streaming model.
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Usage:
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(1) Streaming transducer
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GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26
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cd sherpa-onnx-streaming-zipformer-en-2023-06-26
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git lfs pull --include "*.onnx"
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./python-api-examples/online-decode-files.py \
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--tokens=./sherpa-onnx-streaming-zipformer-en-2023-06-26/tokens.txt \
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--encoder=./sherpa-onnx-streaming-zipformer-en-2023-06-26/encoder-epoch-99-avg-1-chunk-16-left-64.onnx \
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--decoder=./sherpa-onnx-streaming-zipformer-en-2023-06-26/decoder-epoch-99-avg-1-chunk-16-left-64.onnx \
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--joiner=./sherpa-onnx-streaming-zipformer-en-2023-06-26/joiner-epoch-99-avg-1-chunk-16-left-64.onnx \
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./sherpa-onnx-streaming-zipformer-en-2023-06-26/test_wavs/0.wav \
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./sherpa-onnx-streaming-zipformer-en-2023-06-26/test_wavs/1.wav \
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./sherpa-onnx-streaming-zipformer-en-2023-06-26/test_wavs/8k.wav
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(2) Streaming paraformer
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GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-streaming-paraformer-bilingual-zh-en
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cd sherpa-onnx-streaming-paraformer-bilingual-zh-en
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git lfs pull --include "*.onnx"
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./python-api-examples/online-decode-files.py \
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--tokens=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt \
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--paraformer-encoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.int8.onnx \
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--paraformer-decoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.int8.onnx \
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./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/0.wav \
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./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/1.wav \
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./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/2.wav \
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./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/3.wav \
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./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/8k.wav
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Please refer to
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https://k2-fsa.github.io/sherpa/onnx/index.html
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to install sherpa-onnx and to download streaming pre-trained models.
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"""
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import argparse
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import time
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import wave
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from pathlib import Path
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from typing import List, Tuple
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import numpy as np
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import sherpa_onnx
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def get_args():
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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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"--tokens",
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type=str,
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help="Path to tokens.txt",
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)
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parser.add_argument(
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"--encoder",
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type=str,
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help="Path to the transducer encoder model",
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)
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parser.add_argument(
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"--decoder",
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type=str,
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help="Path to the transducer decoder model",
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)
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parser.add_argument(
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"--joiner",
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type=str,
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help="Path to the transducer joiner model",
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)
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parser.add_argument(
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"--paraformer-encoder",
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type=str,
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help="Path to the paraformer encoder model",
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)
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parser.add_argument(
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"--paraformer-decoder",
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type=str,
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help="Path to the paraformer decoder model",
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)
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parser.add_argument(
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"--num-threads",
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type=int,
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default=1,
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help="Number of threads for neural network computation",
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)
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parser.add_argument(
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"--decoding-method",
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type=str,
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default="greedy_search",
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help="Valid values are greedy_search and modified_beam_search",
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)
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parser.add_argument(
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"--max-active-paths",
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type=int,
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default=4,
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help="""Used only when --decoding-method is modified_beam_search.
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It specifies number of active paths to keep during decoding.
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""",
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)
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parser.add_argument(
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"--lm",
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type=str,
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default="",
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help="""Used only when --decoding-method is modified_beam_search.
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path of language model.
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""",
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)
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parser.add_argument(
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"--lm-scale",
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type=float,
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default=0.1,
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help="""Used only when --decoding-method is modified_beam_search.
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scale of language model.
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""",
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)
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parser.add_argument(
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"--provider",
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type=str,
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default="cpu",
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help="Valid values: cpu, cuda, coreml",
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)
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parser.add_argument(
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"--hotwords-file",
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type=str,
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default="",
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help="""
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The file containing hotwords, one words/phrases per line, and for each
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phrase the bpe/cjkchar are separated by a space. For example:
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▁HE LL O ▁WORLD
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你 好 世 界
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""",
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)
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parser.add_argument(
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"--hotwords-score",
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type=float,
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default=1.5,
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help="""
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The hotword score of each token for biasing word/phrase. Used only if
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--hotwords-file is given.
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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 decode. Each file must be of WAVE"
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"format with a single channel, and each sample has 16-bit, "
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"i.e., int16_t. "
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"The sample rate of the file can be arbitrary and does not need to "
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"be 16 kHz",
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)
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return parser.parse_args()
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def assert_file_exists(filename: str):
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assert Path(filename).is_file(), (
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f"{filename} does not exist!\n"
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"Please refer to "
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"https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
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)
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def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
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"""
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Args:
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wave_filename:
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Path to a wave file. It should be single channel and each sample should
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be 16-bit. Its sample rate does not need to be 16kHz.
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Returns:
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Return a tuple containing:
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- A 1-D array of dtype np.float32 containing the samples, which are
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normalized to the range [-1, 1].
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- sample rate of the wave file
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"""
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with wave.open(wave_filename) as f:
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assert f.getnchannels() == 1, f.getnchannels()
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assert f.getsampwidth() == 2, f.getsampwidth() # it is in bytes
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num_samples = f.getnframes()
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samples = f.readframes(num_samples)
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samples_int16 = np.frombuffer(samples, dtype=np.int16)
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samples_float32 = samples_int16.astype(np.float32)
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samples_float32 = samples_float32 / 32768
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return samples_float32, f.getframerate()
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def main():
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args = get_args()
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assert_file_exists(args.tokens)
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if args.encoder:
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assert_file_exists(args.encoder)
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assert_file_exists(args.decoder)
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assert_file_exists(args.joiner)
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assert not args.paraformer_encoder, args.paraformer_encoder
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assert not args.paraformer_decoder, args.paraformer_decoder
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recognizer = sherpa_onnx.OnlineRecognizer.from_transducer(
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tokens=args.tokens,
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encoder=args.encoder,
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decoder=args.decoder,
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joiner=args.joiner,
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num_threads=args.num_threads,
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provider=args.provider,
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sample_rate=16000,
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feature_dim=80,
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decoding_method=args.decoding_method,
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max_active_paths=args.max_active_paths,
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lm=args.lm,
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lm_scale=args.lm_scale,
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hotwords_file=args.hotwords_file,
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hotwords_score=args.hotwords_score,
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)
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elif args.paraformer_encoder:
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recognizer = sherpa_onnx.OnlineRecognizer.from_paraformer(
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tokens=args.tokens,
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encoder=args.paraformer_encoder,
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decoder=args.paraformer_decoder,
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num_threads=args.num_threads,
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provider=args.provider,
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sample_rate=16000,
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feature_dim=80,
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decoding_method="greedy_search",
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)
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else:
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raise ValueError("Please provide a model")
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print("Started!")
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start_time = time.time()
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streams = []
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total_duration = 0
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for wave_filename in args.sound_files:
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assert_file_exists(wave_filename)
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samples, sample_rate = read_wave(wave_filename)
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duration = len(samples) / sample_rate
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total_duration += duration
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s = recognizer.create_stream()
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s.accept_waveform(sample_rate, samples)
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tail_paddings = np.zeros(int(0.66 * sample_rate), dtype=np.float32)
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s.accept_waveform(sample_rate, tail_paddings)
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s.input_finished()
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streams.append(s)
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while True:
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ready_list = []
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for s in streams:
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if recognizer.is_ready(s):
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ready_list.append(s)
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if len(ready_list) == 0:
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break
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recognizer.decode_streams(ready_list)
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results = [recognizer.get_result(s) for s in streams]
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end_time = time.time()
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print("Done!")
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for wave_filename, result in zip(args.sound_files, results):
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print(f"{wave_filename}\n{result}")
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print("-" * 10)
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elapsed_seconds = end_time - start_time
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rtf = elapsed_seconds / total_duration
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print(f"num_threads: {args.num_threads}")
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print(f"decoding_method: {args.decoding_method}")
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print(f"Wave duration: {total_duration:.3f} s")
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print(f"Elapsed time: {elapsed_seconds:.3f} s")
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print(
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f"Real time factor (RTF): {elapsed_seconds:.3f}/{total_duration:.3f} = {rtf:.3f}"
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)
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if __name__ == "__main__":
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main()
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