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decode-file-c-api.c
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decode-file-c-api.c
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// c-api-examples/decode-file-c-api.c
//
// Copyright (c) 2023 Xiaomi Corporation
// This file shows how to use sherpa-onnx C API
// to decode a file.
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "cargs.h"
#include "sherpa-onnx/c-api/c-api.h"
static struct cag_option options[] = {
{.identifier = 'h',
.access_letters = "h",
.access_name = "help",
.description = "Show help"},
{.identifier = 't',
.access_letters = NULL,
.access_name = "tokens",
.value_name = "tokens",
.description = "Tokens file"},
{.identifier = 'e',
.access_letters = NULL,
.access_name = "encoder",
.value_name = "encoder",
.description = "Encoder ONNX file"},
{.identifier = 'd',
.access_letters = NULL,
.access_name = "decoder",
.value_name = "decoder",
.description = "Decoder ONNX file"},
{.identifier = 'j',
.access_letters = NULL,
.access_name = "joiner",
.value_name = "joiner",
.description = "Joiner ONNX file"},
{.identifier = 'n',
.access_letters = NULL,
.access_name = "num-threads",
.value_name = "num-threads",
.description = "Number of threads"},
{.identifier = 'p',
.access_letters = NULL,
.access_name = "provider",
.value_name = "provider",
.description = "Provider: cpu (default), cuda, coreml"},
{.identifier = 'm',
.access_letters = NULL,
.access_name = "decoding-method",
.value_name = "decoding-method",
.description =
"Decoding method: greedy_search (default), modified_beam_search"},
{.identifier = 'f',
.access_letters = NULL,
.access_name = "hotwords-file",
.value_name = "hotwords-file",
.description = "The file containing hotwords, one words/phrases per line, "
"and for each phrase the bpe/cjkchar are separated by a "
"space. For example: ▁HE LL O ▁WORLD, 你 好 世 界"},
{.identifier = 's',
.access_letters = NULL,
.access_name = "hotwords-score",
.value_name = "hotwords-score",
.description = "The bonus score for each token in hotwords. Used only "
"when decoding_method is modified_beam_search"},
};
const char *kUsage =
"\n"
"Usage:\n "
" ./bin/decode-file-c-api \\\n"
" --tokens=/path/to/tokens.txt \\\n"
" --encoder=/path/to/encoder.onnx \\\n"
" --decoder=/path/to/decoder.onnx \\\n"
" --joiner=/path/to/joiner.onnx \\\n"
" --provider=cpu \\\n"
" /path/to/foo.wav\n"
"\n\n"
"Default num_threads is 1.\n"
"Valid decoding_method: greedy_search (default), modified_beam_search\n\n"
"Valid provider: cpu (default), cuda, coreml\n\n"
"Please refer to \n"
"https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/"
"index.html\n"
"for a list of pre-trained models to download.\n"
"\n"
"Note that this file supports only streaming transducer models.\n";
int32_t main(int32_t argc, char *argv[]) {
if (argc < 6) {
fprintf(stderr, "%s\n", kUsage);
exit(0);
}
SherpaOnnxOnlineRecognizerConfig config;
memset(&config, 0, sizeof(config));
config.model_config.debug = 0;
config.model_config.num_threads = 1;
config.model_config.provider = "cpu";
config.decoding_method = "greedy_search";
config.max_active_paths = 4;
config.feat_config.sample_rate = 16000;
config.feat_config.feature_dim = 80;
config.enable_endpoint = 1;
config.rule1_min_trailing_silence = 2.4;
config.rule2_min_trailing_silence = 1.2;
config.rule3_min_utterance_length = 300;
cag_option_context context;
char identifier;
const char *value;
cag_option_prepare(&context, options, CAG_ARRAY_SIZE(options), argc, argv);
while (cag_option_fetch(&context)) {
identifier = cag_option_get(&context);
value = cag_option_get_value(&context);
switch (identifier) {
case 't':
config.model_config.tokens = value;
break;
case 'e':
config.model_config.transducer.encoder = value;
break;
case 'd':
config.model_config.transducer.decoder = value;
break;
case 'j':
config.model_config.transducer.joiner = value;
break;
case 'n':
config.model_config.num_threads = atoi(value);
break;
case 'p':
config.model_config.provider = value;
break;
case 'm':
config.decoding_method = value;
break;
case 'f':
config.hotwords_file = value;
break;
case 's':
config.hotwords_score = atof(value);
break;
case 'h': {
fprintf(stderr, "%s\n", kUsage);
exit(0);
break;
}
default:
// do nothing as config already has valid default values
break;
}
}
const SherpaOnnxOnlineRecognizer *recognizer =
CreateOnlineRecognizer(&config);
const SherpaOnnxOnlineStream *stream = CreateOnlineStream(recognizer);
const SherpaOnnxDisplay *display = CreateDisplay(50);
int32_t segment_id = 0;
const char *wav_filename = argv[context.index];
const SherpaOnnxWave *wave = SherpaOnnxReadWave(wav_filename);
if (wave == NULL) {
fprintf(stderr, "Failed to read %s\n", wav_filename);
return -1;
}
// simulate streaming
#define N 3200 // 0.2 s. Sample rate is fixed to 16 kHz
int16_t buffer[N];
float samples[N];
fprintf(stderr, "sample rate: %d, num samples: %d, duration: %.2f s\n",
wave->sample_rate, wave->num_samples,
(float)wave->num_samples / wave->sample_rate);
int32_t k = 0;
while (k < wave->num_samples) {
int32_t start = k;
int32_t end =
(start + N > wave->num_samples) ? wave->num_samples : (start + N);
k += N;
AcceptWaveform(stream, wave->sample_rate, wave->samples + start,
end - start);
while (IsOnlineStreamReady(recognizer, stream)) {
DecodeOnlineStream(recognizer, stream);
}
const SherpaOnnxOnlineRecognizerResult *r =
GetOnlineStreamResult(recognizer, stream);
if (strlen(r->text)) {
SherpaOnnxPrint(display, segment_id, r->text);
}
if (IsEndpoint(recognizer, stream)) {
if (strlen(r->text)) {
++segment_id;
}
Reset(recognizer, stream);
}
DestroyOnlineRecognizerResult(r);
}
// add some tail padding
float tail_paddings[4800] = {0}; // 0.3 seconds at 16 kHz sample rate
AcceptWaveform(stream, wave->sample_rate, tail_paddings, 4800);
SherpaOnnxFreeWave(wave);
InputFinished(stream);
while (IsOnlineStreamReady(recognizer, stream)) {
DecodeOnlineStream(recognizer, stream);
}
const SherpaOnnxOnlineRecognizerResult *r =
GetOnlineStreamResult(recognizer, stream);
if (strlen(r->text)) {
SherpaOnnxPrint(display, segment_id, r->text);
}
DestroyOnlineRecognizerResult(r);
DestroyDisplay(display);
DestroyOnlineStream(stream);
DestroyOnlineRecognizer(recognizer);
fprintf(stderr, "\n");
return 0;
}