infinite42
Committed by GitHub

support test long audio with streaming-model & vad (#2405)

... ... @@ -68,6 +68,7 @@ def get_binaries():
"sherpa-onnx-vad-microphone",
"sherpa-onnx-vad-microphone-offline-asr",
"sherpa-onnx-vad-with-offline-asr",
"sherpa-onnx-vad-with-online-asr",
"sherpa-onnx-version",
]
... ...
... ... @@ -505,6 +505,10 @@ if(SHERPA_ONNX_ENABLE_PORTAUDIO AND SHERPA_ONNX_ENABLE_BINARY)
sherpa-onnx-vad-with-offline-asr.cc
)
add_executable(sherpa-onnx-vad-with-online-asr
sherpa-onnx-vad-with-online-asr.cc
)
add_executable(sherpa-onnx-vad-microphone-offline-asr
sherpa-onnx-vad-microphone-offline-asr.cc
microphone.cc
... ... @@ -529,6 +533,7 @@ if(SHERPA_ONNX_ENABLE_PORTAUDIO AND SHERPA_ONNX_ENABLE_BINARY)
sherpa-onnx-vad-microphone
sherpa-onnx-vad-microphone-offline-asr
sherpa-onnx-vad-with-offline-asr
sherpa-onnx-vad-with-online-asr
)
if(SHERPA_ONNX_ENABLE_TTS)
list(APPEND exes
... ...
// sherpa-onnx/csrc/sherpa-onnx-vad-with-online-asr.cc
//
// Copyright (c) 2025 Xiaomi Corporation
// Copyright (c) 2025 Pingfeng Luo
//
// This file demonstrates how to use vad in streaming speech recognition
//
#include <stdio.h>
#include <chrono> // NOLINT
#include <string>
#include <vector>
#include "sherpa-onnx/csrc/online-recognizer.h"
#include "sherpa-onnx/csrc/online-stream.h"
#include "sherpa-onnx/csrc/parse-options.h"
#include "sherpa-onnx/csrc/resample.h"
#include "sherpa-onnx/csrc/symbol-table.h"
#include "sherpa-onnx/csrc/voice-activity-detector.h"
#include "sherpa-onnx/csrc/wave-reader.h"
int32_t main(int32_t argc, char *argv[]) {
const char *kUsageMessage = R"usage(
Speech recognition using VAD + streaming models with sherpa-onnx-vad-with-online-asr.
This is useful when testing long audio.
Usage:
Note you can download silero_vad.onnx using
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
(1) Streaming transducer
./bin/sherpa-onnx-vad-with-online-asr \
--silero-vad-model=/path/to/silero_vad.onnx \
--tokens=/path/to/tokens.txt \
--encoder=/path/to/encoder.onnx \
--decoder=/path/to/decoder.onnx \
--joiner=/path/to/joiner.onnx \
--provider=cpu \
--num-threads=2 \
--decoding-method=greedy_search \
/path/to/long_duration.wav
(2) Streaming zipformer2 CTC
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13.tar.bz2
tar xvf sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13.tar.bz2
./bin/sherpa-onnx-vad-with-online-asr \
--debug=1 \
--silero-vad-model=/path/to/silero_vad.onnx \
--zipformer2-ctc-model=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx \
--tokens=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav
(3) Streaming paraformer
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-paraformer-bilingual-zh-en.tar.bz2
tar xvf sherpa-onnx-streaming-paraformer-bilingual-zh-en.tar.bz2
./bin/sherpa-onnx-vad-with-online-asr \
--silero-vad-model=/path/to/silero_vad.onnx \
--tokens=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt \
--paraformer-encoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.onnx \
--paraformer-decoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.onnx \
/path/to/long_duration.wav
The input wav should be of single channel, 16-bit PCM encoded wave file; its
sampling rate can be arbitrary and does not need to be 16kHz.
Please refer to
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
for a list of pre-trained models to download.
)usage";
sherpa_onnx::ParseOptions po(kUsageMessage);
sherpa_onnx::OnlineRecognizerConfig asr_config;
asr_config.Register(&po);
sherpa_onnx::VadModelConfig vad_config;
vad_config.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 1) {
fprintf(stderr, "Error: Please provide exactly 1 wave file. Given: %d\n\n",
po.NumArgs());
po.PrintUsage();
exit(EXIT_FAILURE);
}
fprintf(stderr, "%s\n", vad_config.ToString().c_str());
fprintf(stderr, "%s\n", asr_config.ToString().c_str());
if (!vad_config.Validate()) {
fprintf(stderr, "Errors in vad_config!\n");
return -1;
}
if (!asr_config.Validate()) {
fprintf(stderr, "Errors in ASR config!\n");
return -1;
}
fprintf(stderr, "Creating recognizer ...\n");
sherpa_onnx::OnlineRecognizer recognizer(asr_config);
fprintf(stderr, "Recognizer created!\n");
auto vad = std::make_unique<sherpa_onnx::VoiceActivityDetector>(vad_config);
fprintf(stderr, "Started\n");
const auto begin = std::chrono::steady_clock::now();
std::string wave_filename = po.GetArg(1);
fprintf(stderr, "Reading: %s\n", wave_filename.c_str());
int32_t sampling_rate = -1;
bool is_ok = false;
auto samples = sherpa_onnx::ReadWave(wave_filename, &sampling_rate, &is_ok);
if (!is_ok) {
fprintf(stderr, "Failed to read '%s'\n", wave_filename.c_str());
return -1;
}
if (sampling_rate != 16000) {
fprintf(stderr, "Resampling from %d Hz to 16000 Hz\n", sampling_rate);
float min_freq = std::min(sampling_rate, 16000)
float lowpass_cutoff = 0.99 * 0.5 * min_freq;
int32_t lowpass_filter_width = 6;
auto resampler = std::make_unique<sherpa_onnx::LinearResample>(
sampling_rate, 16000, lowpass_cutoff, lowpass_filter_width);
std::vector<float> out_samples;
resampler->Resample(samples.data(), samples.size(), true, &out_samples);
samples = std::move(out_samples);
fprintf(stderr, "Resampling done\n");
}
fprintf(stderr, "Started!\n");
int32_t window_size = vad_config.ten_vad.model.empty()
? vad_config.silero_vad.window_size : vad_config.ten_vad.window_size;
int32_t offset = 0;
int32_t segment_id = 0;
bool speech_started = false;
while (offset < samples.size()) {
if (offset + window_size <= samples.size()) {
vad->AcceptWaveform(samples.data() + offset, window_size);
} else {
vad->Flush();
}
offset += window_size;
if (vad->IsSpeechDetected() && !speech_started) {
// new voice activity
speech_started = true;
segment_id++;
} else if (!vad->IsSpeechDetected() && speech_started) {
// end voice activity
speech_started = false;
}
while (!vad->Empty()) {
const auto &segment = vad->Front();
float duration = segment.samples.size() / 16000.;
float start_time = segment.start / 16000.;
float end_time = start_time + duration;
auto s = recognizer.CreateStream();
s->AcceptWaveform(16000, segment.samples.data(), segment.samples.size());
s->InputFinished();
while (recognizer.IsReady(s.get())) {
recognizer.DecodeStream(s.get());
}
auto text = recognizer.GetResult(s.get()).text;
if (!text.empty()) {
fprintf(stderr, "vad segment(%d:%.3f-%.3f) results: %s\n",
segment_id, start_time, end_time, text.c_str());
}
vad->Pop();
}
}
const auto end = std::chrono::steady_clock::now();
float elapsed_seconds =
std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
.count() /
1000.;
fprintf(stderr, "num threads: %d\n", asr_config.model_config.num_threads);
fprintf(stderr, "decoding method: %s\n", asr_config.decoding_method.c_str());
if (asr_config.decoding_method == "modified_beam_search") {
fprintf(stderr, "max active paths: %d\n", asr_config.max_active_paths);
}
float duration = samples.size() / 16000.;
fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
float rtf = elapsed_seconds / duration;
fprintf(stderr, "Real time factor (RTF): %.3f / %.3f = %.3f\n",
elapsed_seconds, duration, rtf);
return 0;
}
... ...