Add Python API examples for speaker recognition with VAD and ASR. (#532)
This commit is contained in:
Fangjun Kuang
474
python-api-examples/speaker-identification-with-vad-non-streaming-asr.py
Executable file
474
python-api-examples/speaker-identification-with-vad-non-streaming-asr.py
Executable file
@@ -0,0 +1,474 @@
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#!/usr/bin/env python3
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"""
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This script shows how to use Python APIs for speaker identification with
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a microphone, a VAD model, and a non-streaming ASR model.
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Please see also ./generate-subtitles.py
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Usage:
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(1) Prepare a text file containing speaker related files.
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Each line in the text file contains two columns. The first column is the
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speaker name, while the second column contains the wave file of the speaker.
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If the text file contains multiple wave files for the same speaker, then the
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embeddings of these files are averaged.
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An example text file is given below:
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foo /path/to/a.wav
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bar /path/to/b.wav
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foo /path/to/c.wav
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foobar /path/to/d.wav
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Each wave file should contain only a single channel; the sample format
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should be int16_t; the sample rate can be arbitrary.
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(2) Download a model for computing speaker embeddings
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Please visit
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models
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to download a model. An example is given below:
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/wespeaker_zh_cnceleb_resnet34.onnx
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Note that `zh` means Chinese, while `en` means English.
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(3) Download the VAD model
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Please visit
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https://github.com/snakers4/silero-vad/blob/master/files/silero_vad.onnx
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to download silero_vad.onnx
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For instance,
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wget https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx
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(4) Please refer to ./generate-subtitles.py
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to download a non-streaming ASR model.
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(5) Run this script
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Assume the filename of the text file is speaker.txt.
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python3 ./python-api-examples/speaker-identification-with-vad.py \
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--silero-vad-model=/path/to/silero_vad.onnx \
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--speaker-file ./speaker.txt \
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--model ./wespeaker_zh_cnceleb_resnet34.onnx
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"""
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import argparse
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import sys
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Tuple
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import numpy as np
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import sherpa_onnx
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import torchaudio
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try:
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import sounddevice as sd
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except ImportError:
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print("Please install sounddevice first. You can use")
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print()
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print(" pip install sounddevice")
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print()
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print("to install it")
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sys.exit(-1)
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g_sample_rate = 16000
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def register_non_streaming_asr_model_args(parser):
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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 transducer 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 transducer 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 transducer 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 model.onnx from Paraformer",
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)
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parser.add_argument(
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"--wenet-ctc",
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default="",
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type=str,
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help="Path to the CTC model.onnx from WeNet",
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)
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parser.add_argument(
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"--whisper-encoder",
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default="",
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type=str,
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help="Path to whisper encoder model",
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)
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parser.add_argument(
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"--whisper-decoder",
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default="",
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type=str,
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help="Path to whisper decoder model",
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)
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parser.add_argument(
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"--whisper-language",
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default="",
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type=str,
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help="""It specifies the spoken language in the input file.
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Example values: en, fr, de, zh, jp.
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Available languages for multilingual models can be found at
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https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
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If not specified, we infer the language from the input audio file.
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""",
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)
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parser.add_argument(
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"--whisper-task",
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default="transcribe",
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choices=["transcribe", "translate"],
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type=str,
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help="""For multilingual models, if you specify translate, the output
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will be in English.
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""",
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)
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parser.add_argument(
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"--whisper-tail-paddings",
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default=-1,
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type=int,
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help="""Number of tail padding frames.
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We have removed the 30-second constraint from whisper, so you need to
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choose the amount of tail padding frames by yourself.
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Use -1 to use a default value for tail padding.
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""",
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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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modified_beam_search is valid only for transducer models.
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""",
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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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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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register_non_streaming_asr_model_args(parser)
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parser.add_argument(
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"--speaker-file",
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type=str,
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required=True,
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help="""Path to the speaker file. Read the help doc at the beginning of this
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file for the format.""",
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)
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parser.add_argument(
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"--model",
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type=str,
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required=True,
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help="Path to the speaker embedding model file.",
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)
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parser.add_argument(
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"--silero-vad-model",
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type=str,
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required=True,
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help="Path to silero_vad.onnx",
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)
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parser.add_argument("--threshold", type=float, default=0.6)
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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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"--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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"--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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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 create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
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if args.encoder:
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assert len(args.paraformer) == 0, args.paraformer
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assert len(args.wenet_ctc) == 0, args.wenet_ctc
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assert len(args.whisper_encoder) == 0, args.whisper_encoder
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assert len(args.whisper_decoder) == 0, args.whisper_decoder
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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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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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elif args.paraformer:
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assert len(args.wenet_ctc) == 0, args.wenet_ctc
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assert len(args.whisper_encoder) == 0, args.whisper_encoder
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assert len(args.whisper_decoder) == 0, args.whisper_decoder
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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=g_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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elif args.wenet_ctc:
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assert len(args.whisper_encoder) == 0, args.whisper_encoder
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assert len(args.whisper_decoder) == 0, args.whisper_decoder
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assert_file_exists(args.wenet_ctc)
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recognizer = sherpa_onnx.OfflineRecognizer.from_wenet_ctc(
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model=args.wenet_ctc,
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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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elif args.whisper_encoder:
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assert_file_exists(args.whisper_encoder)
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assert_file_exists(args.whisper_decoder)
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recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
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encoder=args.whisper_encoder,
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decoder=args.whisper_decoder,
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tokens=args.tokens,
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num_threads=args.num_threads,
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decoding_method=args.decoding_method,
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debug=args.debug,
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language=args.whisper_language,
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task=args.whisper_task,
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tail_paddings=args.whisper_tail_paddings,
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)
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else:
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raise ValueError("Please specify at least one model")
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return recognizer
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def load_speaker_embedding_model(args):
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config = sherpa_onnx.SpeakerEmbeddingExtractorConfig(
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model=args.model,
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num_threads=args.num_threads,
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debug=args.debug,
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provider=args.provider,
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)
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if not config.validate():
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raise ValueError(f"Invalid config. {config}")
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extractor = sherpa_onnx.SpeakerEmbeddingExtractor(config)
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return extractor
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def load_speaker_file(args) -> Dict[str, List[str]]:
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if not Path(args.speaker_file).is_file():
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raise ValueError(f"--speaker-file {args.speaker_file} does not exist")
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ans = defaultdict(list)
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with open(args.speaker_file) as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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fields = line.split()
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if len(fields) != 2:
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raise ValueError(f"Invalid line: {line}. Fields: {fields}")
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speaker_name, filename = fields
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ans[speaker_name].append(filename)
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return ans
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def load_audio(filename: str) -> Tuple[np.ndarray, int]:
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samples, sample_rate = torchaudio.load(filename)
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return samples[0].contiguous().numpy(), sample_rate
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def compute_speaker_embedding(
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filenames: List[str],
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extractor: sherpa_onnx.SpeakerEmbeddingExtractor,
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) -> np.ndarray:
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assert len(filenames) > 0, "filenames is empty"
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ans = None
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for filename in filenames:
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print(f"processing {filename}")
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samples, sample_rate = load_audio(filename)
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stream = extractor.create_stream()
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stream.accept_waveform(sample_rate=sample_rate, waveform=samples)
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stream.input_finished()
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assert extractor.is_ready(stream)
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embedding = extractor.compute(stream)
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embedding = np.array(embedding)
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if ans is None:
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ans = embedding
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else:
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ans += embedding
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return ans / len(filenames)
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def main():
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args = get_args()
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print(args)
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recognizer = create_recognizer(args)
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extractor = load_speaker_embedding_model(args)
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speaker_file = load_speaker_file(args)
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manager = sherpa_onnx.SpeakerEmbeddingManager(extractor.dim)
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for name, filename_list in speaker_file.items():
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embedding = compute_speaker_embedding(
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filenames=filename_list,
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extractor=extractor,
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)
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status = manager.add(name, embedding)
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if not status:
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raise RuntimeError(f"Failed to register speaker {name}")
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vad_config = sherpa_onnx.VadModelConfig()
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vad_config.silero_vad.model = args.silero_vad_model
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vad_config.silero_vad.min_silence_duration = 0.25
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vad_config.silero_vad.min_speech_duration = 0.25
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vad_config.sample_rate = g_sample_rate
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window_size = vad_config.silero_vad.window_size
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vad = sherpa_onnx.VoiceActivityDetector(vad_config, buffer_size_in_seconds=100)
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samples_per_read = int(0.1 * g_sample_rate) # 0.1 second = 100 ms
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devices = sd.query_devices()
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if len(devices) == 0:
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print("No microphone devices found")
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sys.exit(0)
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print(devices)
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default_input_device_idx = sd.default.device[0]
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print(f'Use default device: {devices[default_input_device_idx]["name"]}')
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print("Started! Please speak")
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idx = 0
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buffer = []
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with sd.InputStream(channels=1, dtype="float32", samplerate=g_sample_rate) as s:
|
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while True:
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samples, _ = s.read(samples_per_read) # a blocking read
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samples = samples.reshape(-1)
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buffer = np.concatenate([buffer, samples])
|
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while len(buffer) > window_size:
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vad.accept_waveform(buffer[:window_size])
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buffer = buffer[window_size:]
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|
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while not vad.empty():
|
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if len(vad.front.samples) < 0.5 * g_sample_rate:
|
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# this segment is too short, skip it
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vad.pop()
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continue
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stream = extractor.create_stream()
|
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stream.accept_waveform(
|
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sample_rate=g_sample_rate, waveform=vad.front.samples
|
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)
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stream.input_finished()
|
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|
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embedding = extractor.compute(stream)
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embedding = np.array(embedding)
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name = manager.search(embedding, threshold=args.threshold)
|
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if not name:
|
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name = "unknown"
|
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|
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# Now for non-streaming ASR
|
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asr_stream = recognizer.create_stream()
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asr_stream.accept_waveform(
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sample_rate=g_sample_rate, waveform=vad.front.samples
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)
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recognizer.decode_stream(asr_stream)
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text = asr_stream.result.text
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vad.pop()
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||||
print(f"\r{idx}-{name}: {text}")
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idx += 1
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|
||||
|
||||
if __name__ == "__main__":
|
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try:
|
||||
main()
|
||||
except KeyboardInterrupt:
|
||||
print("\nCaught Ctrl + C. Exiting")
|
||||
270
python-api-examples/speaker-identification-with-vad.py
Executable file
270
python-api-examples/speaker-identification-with-vad.py
Executable file
@@ -0,0 +1,270 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
"""
|
||||
This script shows how to use Python APIs for speaker identification with
|
||||
a microphone and a VAD model
|
||||
|
||||
Usage:
|
||||
|
||||
(1) Prepare a text file containing speaker related files.
|
||||
|
||||
Each line in the text file contains two columns. The first column is the
|
||||
speaker name, while the second column contains the wave file of the speaker.
|
||||
|
||||
If the text file contains multiple wave files for the same speaker, then the
|
||||
embeddings of these files are averaged.
|
||||
|
||||
An example text file is given below:
|
||||
|
||||
foo /path/to/a.wav
|
||||
bar /path/to/b.wav
|
||||
foo /path/to/c.wav
|
||||
foobar /path/to/d.wav
|
||||
|
||||
Each wave file should contain only a single channel; the sample format
|
||||
should be int16_t; the sample rate can be arbitrary.
|
||||
|
||||
(2) Download a model for computing speaker embeddings
|
||||
|
||||
Please visit
|
||||
https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models
|
||||
to download a model. An example is given below:
|
||||
|
||||
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/wespeaker_zh_cnceleb_resnet34.onnx
|
||||
|
||||
Note that `zh` means Chinese, while `en` means English.
|
||||
|
||||
(3) Download the VAD model
|
||||
Please visit
|
||||
https://github.com/snakers4/silero-vad/blob/master/files/silero_vad.onnx
|
||||
to download silero_vad.onnx
|
||||
|
||||
For instance,
|
||||
|
||||
wget https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx
|
||||
|
||||
(4) Run this script
|
||||
|
||||
Assume the filename of the text file is speaker.txt.
|
||||
|
||||
python3 ./python-api-examples/speaker-identification-with-vad.py \
|
||||
--silero-vad-model=/path/to/silero_vad.onnx \
|
||||
--speaker-file ./speaker.txt \
|
||||
--model ./wespeaker_zh_cnceleb_resnet34.onnx
|
||||
"""
|
||||
import argparse
|
||||
import sys
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Tuple
|
||||
|
||||
import numpy as np
|
||||
import sherpa_onnx
|
||||
import torchaudio
|
||||
|
||||
try:
|
||||
import sounddevice as sd
|
||||
except ImportError:
|
||||
print("Please install sounddevice first. You can use")
|
||||
print()
|
||||
print(" pip install sounddevice")
|
||||
print()
|
||||
print("to install it")
|
||||
sys.exit(-1)
|
||||
|
||||
|
||||
def get_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--speaker-file",
|
||||
type=str,
|
||||
required=True,
|
||||
help="""Path to the speaker file. Read the help doc at the beginning of this
|
||||
file for the format.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the speaker embedding model file.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--silero-vad-model",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to silero_vad.onnx",
|
||||
)
|
||||
|
||||
parser.add_argument("--threshold", type=float, default=0.6)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-threads",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of threads for neural network computation",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--debug",
|
||||
type=bool,
|
||||
default=False,
|
||||
help="True to show debug messages",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--provider",
|
||||
type=str,
|
||||
default="cpu",
|
||||
help="Valid values: cpu, cuda, coreml",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def load_speaker_embedding_model(args):
|
||||
config = sherpa_onnx.SpeakerEmbeddingExtractorConfig(
|
||||
model=args.model,
|
||||
num_threads=args.num_threads,
|
||||
debug=args.debug,
|
||||
provider=args.provider,
|
||||
)
|
||||
if not config.validate():
|
||||
raise ValueError(f"Invalid config. {config}")
|
||||
extractor = sherpa_onnx.SpeakerEmbeddingExtractor(config)
|
||||
return extractor
|
||||
|
||||
|
||||
def load_speaker_file(args) -> Dict[str, List[str]]:
|
||||
if not Path(args.speaker_file).is_file():
|
||||
raise ValueError(f"--speaker-file {args.speaker_file} does not exist")
|
||||
|
||||
ans = defaultdict(list)
|
||||
with open(args.speaker_file) as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
fields = line.split()
|
||||
if len(fields) != 2:
|
||||
raise ValueError(f"Invalid line: {line}. Fields: {fields}")
|
||||
|
||||
speaker_name, filename = fields
|
||||
ans[speaker_name].append(filename)
|
||||
return ans
|
||||
|
||||
|
||||
def load_audio(filename: str) -> Tuple[np.ndarray, int]:
|
||||
samples, sample_rate = torchaudio.load(filename)
|
||||
return samples[0].contiguous().numpy(), sample_rate
|
||||
|
||||
|
||||
def compute_speaker_embedding(
|
||||
filenames: List[str],
|
||||
extractor: sherpa_onnx.SpeakerEmbeddingExtractor,
|
||||
) -> np.ndarray:
|
||||
assert len(filenames) > 0, "filenames is empty"
|
||||
|
||||
ans = None
|
||||
for filename in filenames:
|
||||
print(f"processing {filename}")
|
||||
samples, sample_rate = load_audio(filename)
|
||||
stream = extractor.create_stream()
|
||||
stream.accept_waveform(sample_rate=sample_rate, waveform=samples)
|
||||
stream.input_finished()
|
||||
|
||||
assert extractor.is_ready(stream)
|
||||
embedding = extractor.compute(stream)
|
||||
embedding = np.array(embedding)
|
||||
if ans is None:
|
||||
ans = embedding
|
||||
else:
|
||||
ans += embedding
|
||||
|
||||
return ans / len(filenames)
|
||||
|
||||
|
||||
g_sample_rate = 16000
|
||||
|
||||
|
||||
def main():
|
||||
args = get_args()
|
||||
print(args)
|
||||
extractor = load_speaker_embedding_model(args)
|
||||
speaker_file = load_speaker_file(args)
|
||||
|
||||
manager = sherpa_onnx.SpeakerEmbeddingManager(extractor.dim)
|
||||
for name, filename_list in speaker_file.items():
|
||||
embedding = compute_speaker_embedding(
|
||||
filenames=filename_list,
|
||||
extractor=extractor,
|
||||
)
|
||||
status = manager.add(name, embedding)
|
||||
if not status:
|
||||
raise RuntimeError(f"Failed to register speaker {name}")
|
||||
|
||||
vad_config = sherpa_onnx.VadModelConfig()
|
||||
vad_config.silero_vad.model = args.silero_vad_model
|
||||
vad_config.silero_vad.min_silence_duration = 0.25
|
||||
vad_config.silero_vad.min_speech_duration = 0.25
|
||||
vad_config.sample_rate = g_sample_rate
|
||||
|
||||
window_size = vad_config.silero_vad.window_size
|
||||
vad = sherpa_onnx.VoiceActivityDetector(vad_config, buffer_size_in_seconds=100)
|
||||
|
||||
samples_per_read = int(0.1 * g_sample_rate) # 0.1 second = 100 ms
|
||||
|
||||
devices = sd.query_devices()
|
||||
if len(devices) == 0:
|
||||
print("No microphone devices found")
|
||||
sys.exit(0)
|
||||
|
||||
print(devices)
|
||||
default_input_device_idx = sd.default.device[0]
|
||||
print(f'Use default device: {devices[default_input_device_idx]["name"]}')
|
||||
|
||||
print("Started! Please speak")
|
||||
|
||||
idx = 0
|
||||
buffer = []
|
||||
with sd.InputStream(channels=1, dtype="float32", samplerate=g_sample_rate) as s:
|
||||
while True:
|
||||
samples, _ = s.read(samples_per_read) # a blocking read
|
||||
samples = samples.reshape(-1)
|
||||
buffer = np.concatenate([buffer, samples])
|
||||
while len(buffer) > window_size:
|
||||
vad.accept_waveform(buffer[:window_size])
|
||||
buffer = buffer[window_size:]
|
||||
|
||||
while not vad.empty():
|
||||
if len(vad.front.samples) < 0.5 * g_sample_rate:
|
||||
# this segment is too short, skip it
|
||||
vad.pop()
|
||||
continue
|
||||
stream = extractor.create_stream()
|
||||
stream.accept_waveform(
|
||||
sample_rate=g_sample_rate, waveform=vad.front.samples
|
||||
)
|
||||
vad.pop()
|
||||
stream.input_finished()
|
||||
|
||||
print("Computing", end="")
|
||||
embedding = extractor.compute(stream)
|
||||
embedding = np.array(embedding)
|
||||
name = manager.search(embedding, threshold=args.threshold)
|
||||
if not name:
|
||||
name = "unknown"
|
||||
print(f"\r{idx}: Predicted name: {name}")
|
||||
idx += 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
except KeyboardInterrupt:
|
||||
print("\nCaught Ctrl + C. Exiting")
|
||||
@@ -1,7 +1,8 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
"""
|
||||
This script shows how to use Python APIs for speaker identification.
|
||||
This script shows how to use Python APIs for speaker identification with
|
||||
a microphone.
|
||||
|
||||
Usage:
|
||||
|
||||
@@ -43,6 +44,7 @@ python3 ./python-api-examples/speaker-identification.py \
|
||||
"""
|
||||
import argparse
|
||||
import queue
|
||||
import sys
|
||||
import threading
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
@@ -151,7 +153,7 @@ def compute_speaker_embedding(
|
||||
filenames: List[str],
|
||||
extractor: sherpa_onnx.SpeakerEmbeddingExtractor,
|
||||
) -> np.ndarray:
|
||||
assert len(filenames) > 0, f"filenames is empty"
|
||||
assert len(filenames) > 0, "filenames is empty"
|
||||
|
||||
ans = None
|
||||
for filename in filenames:
|
||||
@@ -215,7 +217,7 @@ def main():
|
||||
global g_stop
|
||||
global g_read_mic_thread
|
||||
while True:
|
||||
key = input("Press enter to start recording")
|
||||
key = input("Press Enter to start recording")
|
||||
if key.lower() in ("q", "quit"):
|
||||
g_stop = True
|
||||
break
|
||||
@@ -224,7 +226,7 @@ def main():
|
||||
g_buffer.queue.clear()
|
||||
g_read_mic_thread = threading.Thread(target=read_mic)
|
||||
g_read_mic_thread.start()
|
||||
input("Press enter to stop recording")
|
||||
input("Press Enter to stop recording")
|
||||
g_stop = True
|
||||
g_read_mic_thread.join()
|
||||
print("Compute embedding")
|
||||
|
||||
Reference in New Issue
Block a user