adding a python api for offline decode (#110)
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manyeyes
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python-api-examples/offline-decode-files.py
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240
python-api-examples/offline-decode-files.py
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#!/usr/bin/env python3
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#
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# Copyright (c) 2023 by manyeyes
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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 non-streaming model.
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paraformer Usage:
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./python-api-examples/offline-decode-files.py \
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--tokens=/path/to/tokens.txt \
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--paraformer=/path/to/paraformer.onnx \
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--num-threads=2 \
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--decoding-method=greedy_search \
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--debug=false \
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--sample-rate=16000 \
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--feature-dim=80 \
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/path/to/0.wav \
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/path/to/1.wav
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transducer Usage:
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./python-api-examples/offline-decode-files.py \
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--tokens=/path/to/tokens.txt \
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--encoder=/path/to/encoder.onnx \
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--decoder=/path/to/decoder.onnx \
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--joiner=/path/to/joiner.onnx \
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--num-threads=2 \
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--decoding-method=greedy_search \
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--debug=false \
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--sample-rate=16000 \
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--feature-dim=80 \
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/path/to/0.wav \
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/path/to/1.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 the pre-trained models
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used in this file.
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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 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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default="",
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type=str,
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help="Path to the encoder model",
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)
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parser.add_argument(
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"--decoder",
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default="",
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type=str,
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help="Path to the decoder model",
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)
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parser.add_argument(
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"--joiner",
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default="",
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type=str,
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help="Path to the joiner model",
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)
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parser.add_argument(
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"--paraformer",
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default="",
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type=str,
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help="Path to the paraformer 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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"--debug",
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type=bool,
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default=False,
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help="True to show debug messages",
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)
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parser.add_argument(
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"--sample-rate",
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type=int,
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default=16000,
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help="Sample rate of the feature extractor. Must match the one expected by the model. Note: The input sound files can have a different sample rate from this argument.",
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)
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parser.add_argument(
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"--feature-dim",
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type=int,
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default=80,
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help="Feature dimension. Must match the one expected by the model",
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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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assert args.num_threads > 0, args.num_threads
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if len(args.encoder) > 0:
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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 len(args.paraformer) == 0, args.paraformer
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recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
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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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tokens=args.tokens,
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num_threads=args.num_threads,
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sample_rate=args.sample_rate,
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feature_dim=args.feature_dim,
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decoding_method=args.decoding_method,
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debug=args.debug
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)
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else:
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assert_file_exists(args.paraformer)
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recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
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paraformer=args.paraformer,
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tokens=args.tokens,
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num_threads=args.num_threads,
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sample_rate=args.sample_rate,
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feature_dim=args.feature_dim,
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decoding_method=args.decoding_method,
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debug=args.debug
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)
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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(0.2 * sample_rate), dtype=np.float32)
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s.accept_waveform(sample_rate, tail_paddings)
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streams.append(s)
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recognizer.decode_streams(streams)
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results = [s.result.text 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 / 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: {duration:.3f} s")
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print(f"Elapsed time: {elapsed_seconds:.3f} s")
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print(f"Real time factor (RTF): {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}")
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if __name__ == "__main__":
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main()
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