#!/usr/bin/env python3 """ This script shows how to use Python APIs for speaker identification with a microphone. 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) Run this script Assume the filename of the text file is speaker.txt. python3 ./python-api-examples/speaker-identification.py \ --speaker-file ./speaker.txt \ --model ./wespeaker_zh_cnceleb_resnet34.onnx """ import argparse import queue import sys import threading from collections import defaultdict from pathlib import Path from typing import Dict, List, Tuple import numpy as np import sherpa_onnx import soundfile as sf 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 model file.", ) 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]: data, sample_rate = sf.read( filename, always_2d=True, dtype="float32", ) data = data[:, 0] # use only the first channel samples = np.ascontiguousarray(data) return samples, 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_buffer = queue.Queue() g_stop = False g_sample_rate = 16000 g_read_mic_thread = None def read_mic(): print("Please speak!") samples_per_read = int(0.1 * g_sample_rate) # 0.1 second = 100 ms with sd.InputStream(channels=1, dtype="float32", samplerate=g_sample_rate) as s: while not g_stop: samples, _ = s.read(samples_per_read) # a blocking read g_buffer.put(samples) 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}") 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"]}') global g_stop global g_read_mic_thread while True: key = input("Press Enter to start recording") if key.lower() in ("q", "quit"): g_stop = True break g_stop = False g_buffer.queue.clear() g_read_mic_thread = threading.Thread(target=read_mic) g_read_mic_thread.start() input("Press Enter to stop recording") g_stop = True g_read_mic_thread.join() print("Compute embedding") stream = extractor.create_stream() while not g_buffer.empty(): samples = g_buffer.get() stream.accept_waveform(sample_rate=g_sample_rate, waveform=samples) stream.input_finished() embedding = extractor.compute(stream) embedding = np.array(embedding) name = manager.search(embedding, threshold=args.threshold) if not name: name = "unknown" print(f"Predicted name: {name}") if __name__ == "__main__": try: main() except KeyboardInterrupt: print("\nCaught Ctrl + C. Exiting") g_stop = True if g_read_mic_thread.is_alive(): g_read_mic_thread.join()