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Use alsa to read microphone in speaker identification demo. (#605)
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csukuangfj authored Feb 23, 2024
1 parent 16ba7e2 commit 67acd34
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1 change: 1 addition & 0 deletions cmake/cmake_extension.py
Original file line number Diff line number Diff line change
Expand Up @@ -167,6 +167,7 @@ def build_extension(self, ext: setuptools.extension.Extension):
if enable_alsa():
binaries += ["sherpa-onnx-alsa"]
binaries += ["sherpa-onnx-offline-tts-play-alsa"]
binaries += ["sherpa-onnx-alsa-offline-speaker-identification"]

if is_windows():
binaries += ["kaldi-native-fbank-core.dll"]
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1 change: 1 addition & 0 deletions setup.py
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Expand Up @@ -60,6 +60,7 @@ def get_binaries_to_install():
if enable_alsa():
binaries += ["sherpa-onnx-alsa"]
binaries += ["sherpa-onnx-offline-tts-play-alsa"]
binaries += ["sherpa-onnx-alsa-offline-speaker-identification"]

if is_windows():
binaries += ["kaldi-native-fbank-core.dll"]
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2 changes: 2 additions & 0 deletions sherpa-onnx/csrc/CMakeLists.txt
Original file line number Diff line number Diff line change
Expand Up @@ -231,10 +231,12 @@ endif()
if(SHERPA_ONNX_HAS_ALSA AND SHERPA_ONNX_ENABLE_BINARY)
add_executable(sherpa-onnx-alsa sherpa-onnx-alsa.cc alsa.cc)
add_executable(sherpa-onnx-offline-tts-play-alsa sherpa-onnx-offline-tts-play-alsa.cc alsa-play.cc)
add_executable(sherpa-onnx-alsa-offline-speaker-identification sherpa-onnx-alsa-offline-speaker-identification.cc alsa.cc)

set(exes
sherpa-onnx-alsa
sherpa-onnx-offline-tts-play-alsa
sherpa-onnx-alsa-offline-speaker-identification
)
foreach(exe IN LISTS exes)
target_link_libraries(${exe} sherpa-onnx-core)
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287 changes: 287 additions & 0 deletions sherpa-onnx/csrc/sherpa-onnx-alsa-offline-speaker-identification.cc
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@@ -0,0 +1,287 @@
// sherpa-onnx/csrc/sherpa-onnx-alsa-offline-speaker-identification.cc
//
// Copyright (c) 2024 Xiaomi Corporation

#include <signal.h>
#include <stdio.h>
#include <stdlib.h>

#include <algorithm>
#include <fstream>
#include <mutex> // NOLINT
#include <sstream>
#include <thread> // NOLINT

#include "sherpa-onnx/csrc/alsa.h"
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/microphone.h"
#include "sherpa-onnx/csrc/speaker-embedding-extractor.h"
#include "sherpa-onnx/csrc/speaker-embedding-manager.h"
#include "sherpa-onnx/csrc/wave-reader.h"

enum class State {
kIdle,
kRecording,
kComputing,
};

State state = State::kIdle;

// true to stop the program and exit
bool stop = false;

std::vector<float> samples;
std::mutex samples_mutex;

static void DetectKeyPress() {
SHERPA_ONNX_LOGE("\nPress Enter to start");
int32_t key;
while (!stop && (key = getchar())) {
if (key != 0x0a) {
continue;
}

switch (state) {
case State::kIdle:
SHERPA_ONNX_LOGE("\nStart recording. Press Enter to stop recording");
state = State::kRecording;
{
std::lock_guard<std::mutex> lock(samples_mutex);
samples.clear();
}
break;
case State::kRecording:
SHERPA_ONNX_LOGE("\nStop recording. Computing ...");
state = State::kComputing;
break;
case State::kComputing:
break;
}
}
}

static void Record(const char *device_name, int32_t expected_sample_rate) {
sherpa_onnx::Alsa alsa(device_name);

if (alsa.GetExpectedSampleRate() != expected_sample_rate) {
fprintf(stderr, "sample rate: %d != %d\n", alsa.GetExpectedSampleRate(),
expected_sample_rate);
exit(-1);
}

int32_t chunk = 0.1 * alsa.GetActualSampleRate();
while (!stop) {
std::lock_guard<std::mutex> lock(samples_mutex);
const std::vector<float> &s = alsa.Read(chunk);
samples.insert(samples.end(), s.begin(), s.end());
}
}

static void Handler(int32_t sig) {
stop = true;
fprintf(stderr, "\nCaught Ctrl + C. Press Enter to exit\n");
}

static std::vector<std::vector<float>> ComputeEmbeddings(
const std::vector<std::string> &filenames,
sherpa_onnx::SpeakerEmbeddingExtractor *extractor) {
std::vector<std::vector<float>> embedding_list;
embedding_list.reserve(filenames.size());

for (const auto &f : filenames) {
int32_t sampling_rate = -1;

bool is_ok = false;
const std::vector<float> samples =
sherpa_onnx::ReadWave(f, &sampling_rate, &is_ok);

if (!is_ok) {
fprintf(stderr, "Failed to read %s\n", f.c_str());
exit(-1);
}

auto s = extractor->CreateStream();
s->AcceptWaveform(sampling_rate, samples.data(), samples.size());
s->InputFinished();
auto embedding = extractor->Compute(s.get());
embedding_list.push_back(embedding);
}
return embedding_list;
}

static std::unordered_map<std::string, std::vector<std::string>>
ReadSpeakerFile(const std::string &filename) {
std::unordered_map<std::string, std::vector<std::string>> ans;

std::ifstream is(filename);
if (!is) {
fprintf(stderr, "Failed to open %s", filename.c_str());
exit(0);
}

std::string line;
std::string name;
std::string path;

while (std::getline(is, line)) {
std::istringstream iss(line);
name.clear();
path.clear();

iss >> name >> path;
if (!iss || !iss.eof() || name.empty() || path.empty()) {
fprintf(stderr, "Invalid line: %s\n", line.c_str());
exit(-1);
}
ans[name].push_back(path);
}

return ans;
}

int32_t main(int32_t argc, char *argv[]) {
signal(SIGINT, Handler);

const char *kUsageMessage = R"usage(
This program shows how to use non-streaming speaker identification.
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 it !
./bin/sherpa-onnx-alsa-offline-speaker-identification \
--model=/path/to/your-model.onnx \
--speaker-file=/path/to/speaker.txt \
device_name
The device name specifies which microphone to use in case there are several
on your system. You can use
arecord -l
to find all available microphones on your computer. For instance, if it outputs
**** List of CAPTURE Hardware Devices ****
card 3: UACDemoV10 [UACDemoV1.0], device 0: USB Audio [USB Audio]
Subdevices: 1/1
Subdevice #0: subdevice #0
and if you want to select card 3 and the device 0 on that card, please use:
plughw:3,0
as the device_name.
)usage";

sherpa_onnx::ParseOptions po(kUsageMessage);
float threshold = 0.5;
std::string speaker_file;

po.Register("threshold", &threshold,
"Threshold for comparing embedding scores.");

po.Register("speaker-file", &speaker_file, "Path to speaker.txt");

sherpa_onnx::SpeakerEmbeddingExtractorConfig config;
config.Register(&po);

po.Read(argc, argv);
if (po.NumArgs() != 1) {
fprintf(stderr, "Please provide only 1 argument: the device name\n");
po.PrintUsage();
exit(EXIT_FAILURE);
}

fprintf(stderr, "%s\n", config.ToString().c_str());

if (!config.Validate()) {
fprintf(stderr, "Errors in config! Please use --help to view the usage.\n");
return -1;
}

SHERPA_ONNX_LOGE("\nCreating extractor ...");
sherpa_onnx::SpeakerEmbeddingExtractor extractor(config);
SHERPA_ONNX_LOGE("\nextractor created!");

sherpa_onnx::SpeakerEmbeddingManager manager(extractor.Dim());

auto name2files = ReadSpeakerFile(speaker_file);
for (const auto &p : name2files) {
SHERPA_ONNX_LOGE("\nProcessing speaker %s", p.first.c_str());
auto embedding_list = ComputeEmbeddings(p.second, &extractor);
manager.Add(p.first, embedding_list);
}

std::string device_name = po.GetArg(1);
fprintf(stderr, "Use recording device: %s\n", device_name.c_str());
int32_t sample_rate = 16000;

std::thread t(DetectKeyPress);
std::thread t2(Record, device_name.c_str(), sample_rate);

while (!stop) {
switch (state) {
case State::kIdle:
break;
case State::kRecording:
break;
case State::kComputing: {
std::vector<float> buf;
{
std::lock_guard<std::mutex> lock(samples_mutex);
buf = std::move(samples);
}

auto s = extractor.CreateStream();
s->AcceptWaveform(sample_rate, buf.data(), buf.size());
s->InputFinished();
auto embedding = extractor.Compute(s.get());
auto name = manager.Search(embedding.data(), threshold);

if (name.empty()) {
name = "--Unknown--";
}

SHERPA_ONNX_LOGE("\nDone!\nDetected speaker is: %s", name.c_str());

state = State::kIdle;
SHERPA_ONNX_LOGE("\nPress Enter to start");
break;
}
}

using namespace std::chrono_literals;
std::this_thread::sleep_for(20ms); // sleep for 20ms
}

t.join();
t2.join();

return 0;
}
4 changes: 2 additions & 2 deletions sherpa-onnx/csrc/sherpa-onnx-alsa.cc
Original file line number Diff line number Diff line change
Expand Up @@ -34,14 +34,14 @@ int main(int32_t argc, char *argv[]) {
--provider=cpu \
--num-threads=2 \
--decoding-method=greedy_search \
device_name \
device_name
Please refer to
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
for a list of pre-trained models to download.
The device name specifies which microphone to use in case there are several
on you system. You can use
on your system. You can use
arecord -l
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