enginex-mr_series-sherpa-onnx/python-api-examples/online-decode-files.py

#!/usr/bin/env python3

"""
This file demonstrates how to use sherpa-onnx Python API to transcribe
file(s) with a streaming model.

Usage:

(1) Streaming transducer

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26
cd sherpa-onnx-streaming-zipformer-en-2023-06-26
git lfs pull --include "*.onnx"

./python-api-examples/online-decode-files.py \
  --tokens=./sherpa-onnx-streaming-zipformer-en-2023-06-26/tokens.txt \
  --encoder=./sherpa-onnx-streaming-zipformer-en-2023-06-26/encoder-epoch-99-avg-1-chunk-16-left-64.onnx \
  --decoder=./sherpa-onnx-streaming-zipformer-en-2023-06-26/decoder-epoch-99-avg-1-chunk-16-left-64.onnx \
  --joiner=./sherpa-onnx-streaming-zipformer-en-2023-06-26/joiner-epoch-99-avg-1-chunk-16-left-64.onnx \
  ./sherpa-onnx-streaming-zipformer-en-2023-06-26/test_wavs/0.wav \
  ./sherpa-onnx-streaming-zipformer-en-2023-06-26/test_wavs/1.wav \
  ./sherpa-onnx-streaming-zipformer-en-2023-06-26/test_wavs/8k.wav

(2) Streaming paraformer

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-streaming-paraformer-bilingual-zh-en
cd sherpa-onnx-streaming-paraformer-bilingual-zh-en
git lfs pull --include "*.onnx"

./python-api-examples/online-decode-files.py \
  --tokens=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt \
  --paraformer-encoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.int8.onnx \
  --paraformer-decoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.int8.onnx \
  ./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/0.wav \
  ./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/1.wav \
  ./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/2.wav \
  ./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/3.wav \
  ./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/8k.wav

Please refer to
https://k2-fsa.github.io/sherpa/onnx/index.html
to install sherpa-onnx and to download streaming pre-trained models.
"""
import argparse
import time
import wave
from pathlib import Path
from typing import List, Tuple

import numpy as np
import sentencepiece as spm
import sherpa_onnx


def get_args():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--tokens",
        type=str,
        help="Path to tokens.txt",
    )

    parser.add_argument(
        "--encoder",
        type=str,
        help="Path to the transducer encoder model",
    )

    parser.add_argument(
        "--decoder",
        type=str,
        help="Path to the transducer decoder model",
    )

    parser.add_argument(
        "--joiner",
        type=str,
        help="Path to the transducer joiner model",
    )

    parser.add_argument(
        "--paraformer-encoder",
        type=str,
        help="Path to the paraformer encoder model",
    )

    parser.add_argument(
        "--paraformer-decoder",
        type=str,
        help="Path to the paraformer decoder model",
    )

    parser.add_argument(
        "--num-threads",
        type=int,
        default=1,
        help="Number of threads for neural network computation",
    )

    parser.add_argument(
        "--decoding-method",
        type=str,
        default="greedy_search",
        help="Valid values are greedy_search and modified_beam_search",
    )

    parser.add_argument(
        "--max-active-paths",
        type=int,
        default=4,
        help="""Used only when --decoding-method is modified_beam_search.
        It specifies number of active paths to keep during decoding.
        """,
    )

    parser.add_argument(
        "--provider",
        type=str,
        default="cpu",
        help="Valid values: cpu, cuda, coreml",
    )

    parser.add_argument(
        "--bpe-model",
        type=str,
        default="",
        help="""
        Path to bpe.model, it will be used to tokenize contexts biasing phrases.
        Used only when --decoding-method=modified_beam_search
        """,
    )

    parser.add_argument(
        "--modeling-unit",
        type=str,
        default="char",
        help="""
        The type of modeling unit, it will be used to tokenize contexts biasing phrases.
        Valid values are bpe, bpe+char, char.
        Note: the char here means characters in CJK languages.
        Used only when --decoding-method=modified_beam_search
        """,
    )

    parser.add_argument(
        "--contexts",
        type=str,
        default="",
        help="""
        The context list, it is a string containing some words/phrases separated
        with /, for example, 'HELLO WORLD/I LOVE YOU/GO AWAY".
        Used only when --decoding-method=modified_beam_search
        """,
    )

    parser.add_argument(
        "--context-score",
        type=float,
        default=1.5,
        help="""
        The context score of each token for biasing word/phrase. Used only if
        --contexts is given.
        Used only when --decoding-method=modified_beam_search
        """,
    )

    parser.add_argument(
        "sound_files",
        type=str,
        nargs="+",
        help="The input sound file(s) to decode. Each file must be of WAVE"
        "format with a single channel, and each sample has 16-bit, "
        "i.e., int16_t. "
        "The sample rate of the file can be arbitrary and does not need to "
        "be 16 kHz",
    )

    return parser.parse_args()


def assert_file_exists(filename: str):
    assert Path(filename).is_file(), (
        f"{filename} does not exist!\n"
        "Please refer to "
        "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
    )


def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
    """
    Args:
      wave_filename:
        Path to a wave file. It should be single channel and each sample should
        be 16-bit. Its sample rate does not need to be 16kHz.
    Returns:
      Return a tuple containing:
       - A 1-D array of dtype np.float32 containing the samples, which are
       normalized to the range [-1, 1].
       - sample rate of the wave file
    """

    with wave.open(wave_filename) as f:
        assert f.getnchannels() == 1, f.getnchannels()
        assert f.getsampwidth() == 2, f.getsampwidth()  # it is in bytes
        num_samples = f.getnframes()
        samples = f.readframes(num_samples)
        samples_int16 = np.frombuffer(samples, dtype=np.int16)
        samples_float32 = samples_int16.astype(np.float32)

        samples_float32 = samples_float32 / 32768
        return samples_float32, f.getframerate()


def encode_contexts(args, contexts: List[str]) -> List[List[int]]:
    sp = None
    if "bpe" in args.modeling_unit:
        assert_file_exists(args.bpe_model)
        sp = spm.SentencePieceProcessor()
        sp.load(args.bpe_model)
    tokens = {}
    with open(args.tokens, "r", encoding="utf-8") as f:
        for line in f:
            toks = line.strip().split()
            assert len(toks) == 2, len(toks)
            assert toks[0] not in tokens, f"Duplicate token: {toks} "
            tokens[toks[0]] = int(toks[1])
    return sherpa_onnx.encode_contexts(
        modeling_unit=args.modeling_unit,
        contexts=contexts,
        sp=sp,
        tokens_table=tokens,
    )


def main():
    args = get_args()
    assert_file_exists(args.tokens)

    if args.encoder:
        assert_file_exists(args.encoder)
        assert_file_exists(args.decoder)
        assert_file_exists(args.joiner)

        assert not args.paraformer_encoder, args.paraformer_encoder
        assert not args.paraformer_decoder, args.paraformer_decoder

        recognizer = sherpa_onnx.OnlineRecognizer.from_transducer(
            tokens=args.tokens,
            encoder=args.encoder,
            decoder=args.decoder,
            joiner=args.joiner,
            num_threads=args.num_threads,
            provider=args.provider,
            sample_rate=16000,
            feature_dim=80,
            decoding_method=args.decoding_method,
            max_active_paths=args.max_active_paths,
            context_score=args.context_score,
        )
    elif args.paraformer_encoder:
        recognizer = sherpa_onnx.OnlineRecognizer.from_paraformer(
            tokens=args.tokens,
            encoder=args.paraformer_encoder,
            decoder=args.paraformer_decoder,
            num_threads=args.num_threads,
            provider=args.provider,
            sample_rate=16000,
            feature_dim=80,
            decoding_method="greedy_search",
        )
    else:
        raise ValueError("Please provide a model")

    print("Started!")
    start_time = time.time()

    contexts_list = []
    contexts = [x.strip().upper() for x in args.contexts.split("/") if x.strip()]
    if contexts:
        print(f"Contexts list: {contexts}")
        contexts_list = encode_contexts(args, contexts)

    streams = []
    total_duration = 0
    for wave_filename in args.sound_files:
        assert_file_exists(wave_filename)
        samples, sample_rate = read_wave(wave_filename)
        duration = len(samples) / sample_rate
        total_duration += duration

        if contexts_list:
            s = recognizer.create_stream(contexts_list=contexts_list)
        else:
            s = recognizer.create_stream()

        s.accept_waveform(sample_rate, samples)

        tail_paddings = np.zeros(int(0.66 * sample_rate), dtype=np.float32)
        s.accept_waveform(sample_rate, tail_paddings)

        s.input_finished()

        streams.append(s)

    while True:
        ready_list = []
        for s in streams:
            if recognizer.is_ready(s):
                ready_list.append(s)
        if len(ready_list) == 0:
            break
        recognizer.decode_streams(ready_list)
    results = [recognizer.get_result(s) for s in streams]
    end_time = time.time()
    print("Done!")

    for wave_filename, result in zip(args.sound_files, results):
        print(f"{wave_filename}\n{result}")
        print("-" * 10)

    elapsed_seconds = end_time - start_time
    rtf = elapsed_seconds / total_duration
    print(f"num_threads: {args.num_threads}")
    print(f"decoding_method: {args.decoding_method}")
    print(f"Wave duration: {total_duration:.3f} s")
    print(f"Elapsed time: {elapsed_seconds:.3f} s")
    print(
        f"Real time factor (RTF): {elapsed_seconds:.3f}/{total_duration:.3f} = {rtf:.3f}"
    )


if __name__ == "__main__":
    main()