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Fangjun Kuang
Committed by GitHub
a4bff28e 1 parent b094868f

Support TDNN models from the yesno recipe from icefall (#262)

内嵌 并排对比
正在显示 23 个修改的文件 包含 613 行增加 和 37 行删除
... ... @@ -14,6 +14,50 @@ echo "PATH: $PATH"
which $EXE
log "------------------------------------------------------------"
log "Run tdnn yesno (Hebrew)"
log "------------------------------------------------------------"
repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-tdnn-yesno
log "Start testing ${repo_url}"
repo=$(basename $repo_url)
log "Download pretrained model and test-data from $repo_url"
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
pushd $repo
git lfs pull --include "*.onnx"
ls -lh *.onnx
popd
log "test float32 models"
time $EXE \
--sample-rate=8000 \
--feat-dim=23 \
\
--tokens=$repo/tokens.txt \
--tdnn-model=$repo/model-epoch-14-avg-2.onnx \
$repo/test_wavs/0_0_0_1_0_0_0_1.wav \
$repo/test_wavs/0_0_1_0_0_0_1_0.wav \
$repo/test_wavs/0_0_1_0_0_1_1_1.wav \
$repo/test_wavs/0_0_1_0_1_0_0_1.wav \
$repo/test_wavs/0_0_1_1_0_0_0_1.wav \
$repo/test_wavs/0_0_1_1_0_1_1_0.wav
log "test int8 models"
time $EXE \
--sample-rate=8000 \
--feat-dim=23 \
\
--tokens=$repo/tokens.txt \
--tdnn-model=$repo/model-epoch-14-avg-2.int8.onnx \
$repo/test_wavs/0_0_0_1_0_0_0_1.wav \
$repo/test_wavs/0_0_1_0_0_0_1_0.wav \
$repo/test_wavs/0_0_1_0_0_1_1_1.wav \
$repo/test_wavs/0_0_1_0_1_0_0_1.wav \
$repo/test_wavs/0_0_1_1_0_0_0_1.wav \
$repo/test_wavs/0_0_1_1_0_1_1_0.wav
rm -rf $repo
log "------------------------------------------------------------"
log "Run Citrinet (stt_en_citrinet_512, English)"
log "------------------------------------------------------------"
... ...
... ... @@ -24,7 +24,7 @@ jobs:
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
model_type: ["transducer", "paraformer", "nemo_ctc", "whisper"]
model_type: ["transducer", "paraformer", "nemo_ctc", "whisper", "tdnn"]
steps:
- uses: actions/checkout@v2
... ... @@ -172,3 +172,41 @@ jobs:
./sherpa-onnx-whisper-tiny.en/test_wavs/0.wav \
./sherpa-onnx-whisper-tiny.en/test_wavs/1.wav \
./sherpa-onnx-whisper-tiny.en/test_wavs/8k.wav
- name: Start server for tdnn models
if: matrix.model_type == 'tdnn'
shell: bash
run: |
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-tdnn-yesno
cd sherpa-onnx-tdnn-yesno
git lfs pull --include "*.onnx"
cd ..
python3 ./python-api-examples/non_streaming_server.py \
--tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \
--tokens=./sherpa-onnx-tdnn-yesno/tokens.txt \
--sample-rate=8000 \
--feat-dim=23 &
echo "sleep 10 seconds to wait the server start"
sleep 10
- name: Start client for tdnn models
if: matrix.model_type == 'tdnn'
shell: bash
run: |
python3 ./python-api-examples/offline-websocket-client-decode-files-paralell.py \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_1_1_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_1_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_1_1_0.wav
python3 ./python-api-examples/offline-websocket-client-decode-files-sequential.py \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_1_1_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_1_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_1_1_0.wav
... ...
cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
project(sherpa-onnx)
set(SHERPA_ONNX_VERSION "1.7.2")
set(SHERPA_ONNX_VERSION "1.7.3")
# Disable warning about
#
... ...
... ... @@ -71,6 +71,20 @@ python3 ./python-api-examples/non_streaming_server.py \
--whisper-decoder=./sherpa-onnx-whisper-tiny.en/tiny.en-decoder.onnx \
--tokens=./sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt
(5) Use a tdnn model of the yesno recipe from icefall
cd /path/to/sherpa-onnx
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-tdnn-yesno
cd sherpa-onnx-tdnn-yesno
git lfs pull --include "*.onnx"
python3 ./python-api-examples/non_streaming_server.py \
--sample-rate=8000 \
--feat-dim=23 \
--tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \
--tokens=./sherpa-onnx-tdnn-yesno/tokens.txt
----
To use a certificate so that you can use https, please use
... ... @@ -196,6 +210,15 @@ def add_nemo_ctc_model_args(parser: argparse.ArgumentParser):
)
def add_tdnn_ctc_model_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--tdnn-model",
default="",
type=str,
help="Path to the model.onnx for the tdnn model of the yesno recipe",
)
def add_whisper_model_args(parser: argparse.ArgumentParser):
parser.add_argument(
"--whisper-encoder",
... ... @@ -216,6 +239,7 @@ def add_model_args(parser: argparse.ArgumentParser):
add_transducer_model_args(parser)
add_paraformer_model_args(parser)
add_nemo_ctc_model_args(parser)
add_tdnn_ctc_model_args(parser)
add_whisper_model_args(parser)
parser.add_argument(
... ... @@ -730,6 +754,7 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
assert len(args.nemo_ctc) == 0, args.nemo_ctc
assert len(args.whisper_encoder) == 0, args.whisper_encoder
assert len(args.whisper_decoder) == 0, args.whisper_decoder
assert len(args.tdnn_model) == 0, args.tdnn_model
assert_file_exists(args.encoder)
assert_file_exists(args.decoder)
... ... @@ -750,6 +775,7 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
assert len(args.nemo_ctc) == 0, args.nemo_ctc
assert len(args.whisper_encoder) == 0, args.whisper_encoder
assert len(args.whisper_decoder) == 0, args.whisper_decoder
assert len(args.tdnn_model) == 0, args.tdnn_model
assert_file_exists(args.paraformer)
... ... @@ -764,6 +790,7 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
elif args.nemo_ctc:
assert len(args.whisper_encoder) == 0, args.whisper_encoder
assert len(args.whisper_decoder) == 0, args.whisper_decoder
assert len(args.tdnn_model) == 0, args.tdnn_model
assert_file_exists(args.nemo_ctc)
... ... @@ -776,6 +803,7 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
decoding_method=args.decoding_method,
)
elif args.whisper_encoder:
assert len(args.tdnn_model) == 0, args.tdnn_model
assert_file_exists(args.whisper_encoder)
assert_file_exists(args.whisper_decoder)
... ... @@ -786,6 +814,17 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
num_threads=args.num_threads,
decoding_method=args.decoding_method,
)
elif args.tdnn_model:
assert_file_exists(args.tdnn_model)
recognizer = sherpa_onnx.OfflineRecognizer.from_tdnn_ctc(
model=args.tdnn_model,
tokens=args.tokens,
sample_rate=args.sample_rate,
feature_dim=args.feat_dim,
num_threads=args.num_threads,
decoding_method=args.decoding_method,
)
else:
raise ValueError("Please specify at least one model")
... ...
... ... @@ -8,6 +8,7 @@ This file demonstrates how to use sherpa-onnx Python API to transcribe
file(s) with a non-streaming model.
(1) For paraformer
./python-api-examples/offline-decode-files.py \
--tokens=/path/to/tokens.txt \
--paraformer=/path/to/paraformer.onnx \
... ... @@ -20,6 +21,7 @@ file(s) with a non-streaming model.
/path/to/1.wav
(2) For transducer models from icefall
./python-api-examples/offline-decode-files.py \
--tokens=/path/to/tokens.txt \
--encoder=/path/to/encoder.onnx \
... ... @@ -56,9 +58,20 @@ python3 ./python-api-examples/offline-decode-files.py \
./sherpa-onnx-whisper-base.en/test_wavs/1.wav \
./sherpa-onnx-whisper-base.en/test_wavs/8k.wav
(5) For tdnn models of the yesno recipe from icefall
python3 ./python-api-examples/offline-decode-files.py \
--sample-rate=8000 \
--feature-dim=23 \
--tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \
--tokens=./sherpa-onnx-tdnn-yesno/tokens.txt \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_1_1_1.wav
Please refer to
https://k2-fsa.github.io/sherpa/onnx/index.html
to install sherpa-onnx and to download the pre-trained models
to install sherpa-onnx and to download non-streaming pre-trained models
used in this file.
"""
import argparse
... ... @@ -160,6 +173,13 @@ def get_args():
)
parser.add_argument(
"--tdnn-model",
default="",
type=str,
help="Path to the model.onnx for the tdnn model of the yesno recipe",
)
parser.add_argument(
"--num-threads",
type=int,
default=1,
... ... @@ -285,6 +305,7 @@ def main():
assert len(args.nemo_ctc) == 0, args.nemo_ctc
assert len(args.whisper_encoder) == 0, args.whisper_encoder
assert len(args.whisper_decoder) == 0, args.whisper_decoder
assert len(args.tdnn_model) == 0, args.tdnn_model
contexts = [x.strip().upper() for x in args.contexts.split("/") if x.strip()]
if contexts:
... ... @@ -311,6 +332,7 @@ def main():
assert len(args.nemo_ctc) == 0, args.nemo_ctc
assert len(args.whisper_encoder) == 0, args.whisper_encoder
assert len(args.whisper_decoder) == 0, args.whisper_decoder
assert len(args.tdnn_model) == 0, args.tdnn_model
assert_file_exists(args.paraformer)
... ... @@ -326,6 +348,7 @@ def main():
elif args.nemo_ctc:
assert len(args.whisper_encoder) == 0, args.whisper_encoder
assert len(args.whisper_decoder) == 0, args.whisper_decoder
assert len(args.tdnn_model) == 0, args.tdnn_model
assert_file_exists(args.nemo_ctc)
... ... @@ -339,6 +362,7 @@ def main():
debug=args.debug,
)
elif args.whisper_encoder:
assert len(args.tdnn_model) == 0, args.tdnn_model
assert_file_exists(args.whisper_encoder)
assert_file_exists(args.whisper_decoder)
... ... @@ -347,6 +371,20 @@ def main():
decoder=args.whisper_decoder,
tokens=args.tokens,
num_threads=args.num_threads,
sample_rate=args.sample_rate,
feature_dim=args.feature_dim,
decoding_method=args.decoding_method,
debug=args.debug,
)
elif args.tdnn_model:
assert_file_exists(args.tdnn_model)
recognizer = sherpa_onnx.OfflineRecognizer.from_tdnn_ctc(
model=args.tdnn_model,
tokens=args.tokens,
sample_rate=args.sample_rate,
feature_dim=args.feature_dim,
num_threads=args.num_threads,
decoding_method=args.decoding_method,
debug=args.debug,
)
... ...
... ... @@ -97,20 +97,18 @@ function onFileChange() {
console.log('file.type ' + file.type);
console.log('file.size ' + file.size);
let audioCtx = new AudioContext({sampleRate: 16000});
let reader = new FileReader();
reader.onload = function() {
console.log('reading file!');
let view = new Int16Array(reader.result);
// we assume the input file is a wav file.
// TODO: add some checks here.
let int16_samples = view.subarray(22); // header has 44 bytes == 22 shorts
let num_samples = int16_samples.length;
let float32_samples = new Float32Array(num_samples);
console.log('num_samples ' + num_samples)
for (let i = 0; i < num_samples; ++i) {
float32_samples[i] = int16_samples[i] / 32768.
}
audioCtx.decodeAudioData(reader.result, decodedDone);
};
function decodedDone(decoded) {
let typedArray = new Float32Array(decoded.length);
let float32_samples = decoded.getChannelData(0);
let buf = float32_samples.buffer
// Send 1024 audio samples per request.
//
... ... @@ -119,14 +117,13 @@ function onFileChange() {
// (2) There is a limit on the number of bytes in the payload that can be
// sent by websocket, which is 1MB, I think. We can send a large
// audio file for decoding in this approach.
let buf = float32_samples.buffer
let n = 1024 * 4; // send this number of bytes per request.
console.log('buf length, ' + buf.byteLength);
send_header(buf.byteLength);
for (let start = 0; start < buf.byteLength; start += n) {
socket.send(buf.slice(start, start + n));
}
};
}
reader.readAsArrayBuffer(file);
}
... ...
... ... @@ -32,6 +32,8 @@ set(sources
offline-recognizer.cc
offline-rnn-lm.cc
offline-stream.cc
offline-tdnn-ctc-model.cc
offline-tdnn-model-config.cc
offline-transducer-greedy-search-decoder.cc
offline-transducer-model-config.cc
offline-transducer-model.cc
... ...
... ... @@ -11,12 +11,14 @@
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model.h"
#include "sherpa-onnx/csrc/offline-tdnn-ctc-model.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
namespace {
enum class ModelType {
kEncDecCTCModelBPE,
kTdnn,
kUnkown,
};
... ... @@ -55,6 +57,8 @@ static ModelType GetModelType(char *model_data, size_t model_data_length,
if (model_type.get() == std::string("EncDecCTCModelBPE")) {
return ModelType::kEncDecCTCModelBPE;
} else if (model_type.get() == std::string("tdnn")) {
return ModelType::kTdnn;
} else {
SHERPA_ONNX_LOGE("Unsupported model_type: %s", model_type.get());
return ModelType::kUnkown;
... ... @@ -65,8 +69,18 @@ std::unique_ptr<OfflineCtcModel> OfflineCtcModel::Create(
const OfflineModelConfig &config) {
ModelType model_type = ModelType::kUnkown;
std::string filename;
if (!config.nemo_ctc.model.empty()) {
filename = config.nemo_ctc.model;
} else if (!config.tdnn.model.empty()) {
filename = config.tdnn.model;
} else {
SHERPA_ONNX_LOGE("Please specify a CTC model");
exit(-1);
}
{
auto buffer = ReadFile(config.nemo_ctc.model);
auto buffer = ReadFile(filename);
model_type = GetModelType(buffer.data(), buffer.size(), config.debug);
}
... ... @@ -75,6 +89,9 @@ std::unique_ptr<OfflineCtcModel> OfflineCtcModel::Create(
case ModelType::kEncDecCTCModelBPE:
return std::make_unique<OfflineNemoEncDecCtcModel>(config);
break;
case ModelType::kTdnn:
return std::make_unique<OfflineTdnnCtcModel>(config);
break;
case ModelType::kUnkown:
SHERPA_ONNX_LOGE("Unknown model type in offline CTC!");
return nullptr;
... ...
... ... @@ -39,10 +39,10 @@ class OfflineCtcModel {
/** SubsamplingFactor of the model
*
* For Citrinet, the subsampling factor is usually 4.
* For Conformer CTC, the subsampling factor is usually 8.
* For NeMo Citrinet, the subsampling factor is usually 4.
* For NeMo Conformer CTC, the subsampling factor is usually 8.
*/
virtual int32_t SubsamplingFactor() const = 0;
virtual int32_t SubsamplingFactor() const { return 1; }
/** Return an allocator for allocating memory
*/
... ...
... ... @@ -15,6 +15,7 @@ void OfflineModelConfig::Register(ParseOptions *po) {
paraformer.Register(po);
nemo_ctc.Register(po);
whisper.Register(po);
tdnn.Register(po);
po->Register("tokens", &tokens, "Path to tokens.txt");
... ... @@ -29,7 +30,8 @@ void OfflineModelConfig::Register(ParseOptions *po) {
po->Register("model-type", &model_type,
"Specify it to reduce model initialization time. "
"Valid values are: transducer, paraformer, nemo_ctc, whisper."
"Valid values are: transducer, paraformer, nemo_ctc, whisper, "
"tdnn."
"All other values lead to loading the model twice.");
}
... ... @@ -56,6 +58,10 @@ bool OfflineModelConfig::Validate() const {
return whisper.Validate();
}
if (!tdnn.model.empty()) {
return tdnn.Validate();
}
return transducer.Validate();
}
... ... @@ -67,6 +73,7 @@ std::string OfflineModelConfig::ToString() const {
os << "paraformer=" << paraformer.ToString() << ", ";
os << "nemo_ctc=" << nemo_ctc.ToString() << ", ";
os << "whisper=" << whisper.ToString() << ", ";
os << "tdnn=" << tdnn.ToString() << ", ";
os << "tokens=\"" << tokens << "\", ";
os << "num_threads=" << num_threads << ", ";
os << "debug=" << (debug ? "True" : "False") << ", ";
... ...
... ... @@ -8,6 +8,7 @@
#include "sherpa-onnx/csrc/offline-nemo-enc-dec-ctc-model-config.h"
#include "sherpa-onnx/csrc/offline-paraformer-model-config.h"
#include "sherpa-onnx/csrc/offline-tdnn-model-config.h"
#include "sherpa-onnx/csrc/offline-transducer-model-config.h"
#include "sherpa-onnx/csrc/offline-whisper-model-config.h"
... ... @@ -18,6 +19,7 @@ struct OfflineModelConfig {
OfflineParaformerModelConfig paraformer;
OfflineNemoEncDecCtcModelConfig nemo_ctc;
OfflineWhisperModelConfig whisper;
OfflineTdnnModelConfig tdnn;
std::string tokens;
int32_t num_threads = 2;
... ... @@ -40,12 +42,14 @@ struct OfflineModelConfig {
const OfflineParaformerModelConfig &paraformer,
const OfflineNemoEncDecCtcModelConfig &nemo_ctc,
const OfflineWhisperModelConfig &whisper,
const OfflineTdnnModelConfig &tdnn,
const std::string &tokens, int32_t num_threads, bool debug,
const std::string &provider, const std::string &model_type)
: transducer(transducer),
paraformer(paraformer),
nemo_ctc(nemo_ctc),
whisper(whisper),
tdnn(tdnn),
tokens(tokens),
num_threads(num_threads),
debug(debug),
... ...
... ... @@ -27,6 +27,10 @@ static OfflineRecognitionResult Convert(const OfflineCtcDecoderResult &src,
std::string text;
for (int32_t i = 0; i != src.tokens.size(); ++i) {
if (sym_table.contains("SIL") && src.tokens[i] == sym_table["SIL"]) {
// tdnn models from yesno have a SIL token, we should remove it.
continue;
}
auto sym = sym_table[src.tokens[i]];
text.append(sym);
r.tokens.push_back(std::move(sym));
... ... @@ -46,14 +50,22 @@ class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
model_->FeatureNormalizationMethod();
if (config.decoding_method == "greedy_search") {
if (!symbol_table_.contains("<blk>")) {
if (!symbol_table_.contains("<blk>") &&
!symbol_table_.contains("<eps>")) {
SHERPA_ONNX_LOGE(
"We expect that tokens.txt contains "
"the symbol <blk> and its ID.");
"the symbol <blk> or <eps> and its ID.");
exit(-1);
}
int32_t blank_id = symbol_table_["<blk>"];
int32_t blank_id = 0;
if (symbol_table_.contains("<blk>")) {
blank_id = symbol_table_["<blk>"];
} else if (symbol_table_.contains("<eps>")) {
// for tdnn models of the yesno recipe from icefall
blank_id = symbol_table_["<eps>"];
}
decoder_ = std::make_unique<OfflineCtcGreedySearchDecoder>(blank_id);
} else {
SHERPA_ONNX_LOGE("Only greedy_search is supported at present. Given %s",
... ...
... ... @@ -27,6 +27,8 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
return std::make_unique<OfflineRecognizerParaformerImpl>(config);
} else if (model_type == "nemo_ctc") {
return std::make_unique<OfflineRecognizerCtcImpl>(config);
} else if (model_type == "tdnn") {
return std::make_unique<OfflineRecognizerCtcImpl>(config);
} else if (model_type == "whisper") {
return std::make_unique<OfflineRecognizerWhisperImpl>(config);
} else {
... ... @@ -46,6 +48,8 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
model_filename = config.model_config.paraformer.model;
} else if (!config.model_config.nemo_ctc.model.empty()) {
model_filename = config.model_config.nemo_ctc.model;
} else if (!config.model_config.tdnn.model.empty()) {
model_filename = config.model_config.tdnn.model;
} else if (!config.model_config.whisper.encoder.empty()) {
model_filename = config.model_config.whisper.encoder;
} else {
... ... @@ -84,6 +88,11 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
"paraformer-onnxruntime-python-example/blob/main/add-model-metadata.py"
"\n "
"(3) Whisper"
"\n "
"(4) Tdnn models of the yesno recipe from icefall"
"\n "
"https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR/tdnn"
"\n"
"\n");
exit(-1);
}
... ... @@ -102,6 +111,10 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
return std::make_unique<OfflineRecognizerCtcImpl>(config);
}
if (model_type == "tdnn") {
return std::make_unique<OfflineRecognizerCtcImpl>(config);
}
if (strncmp(model_type.c_str(), "whisper", 7) == 0) {
return std::make_unique<OfflineRecognizerWhisperImpl>(config);
}
... ... @@ -112,7 +125,8 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
" - Non-streaming transducer models from icefall\n"
" - Non-streaming Paraformer models from FunASR\n"
" - EncDecCTCModelBPE models from NeMo\n"
" - Whisper models\n",
" - Whisper models\n"
" - Tdnn models\n",
model_type.c_str());
exit(-1);
... ...
// sherpa-onnx/csrc/offline-tdnn-ctc-model.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/offline-tdnn-ctc-model.h"
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#include "sherpa-onnx/csrc/session.h"
#include "sherpa-onnx/csrc/text-utils.h"
#include "sherpa-onnx/csrc/transpose.h"
namespace sherpa_onnx {
class OfflineTdnnCtcModel::Impl {
public:
explicit Impl(const OfflineModelConfig &config)
: config_(config),
env_(ORT_LOGGING_LEVEL_ERROR),
sess_opts_(GetSessionOptions(config)),
allocator_{} {
Init();
}
std::pair<Ort::Value, Ort::Value> Forward(Ort::Value features) {
auto nnet_out =
sess_->Run({}, input_names_ptr_.data(), &features, 1,
output_names_ptr_.data(), output_names_ptr_.size());
std::vector<int64_t> nnet_out_shape =
nnet_out[0].GetTensorTypeAndShapeInfo().GetShape();
std::vector<int64_t> out_length_vec(nnet_out_shape[0], nnet_out_shape[1]);
std::vector<int64_t> out_length_shape(1, nnet_out_shape[0]);
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
Ort::Value nnet_out_length = Ort::Value::CreateTensor(
memory_info, out_length_vec.data(), out_length_vec.size(),
out_length_shape.data(), out_length_shape.size());
return {std::move(nnet_out[0]), Clone(Allocator(), &nnet_out_length)};
}
int32_t VocabSize() const { return vocab_size_; }
OrtAllocator *Allocator() const { return allocator_; }
private:
void Init() {
auto buf = ReadFile(config_.tdnn.model);
sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
sess_opts_);
GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
PrintModelMetadata(os, meta_data);
SHERPA_ONNX_LOGE("%s\n", os.str().c_str());
}
Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
}
private:
OfflineModelConfig config_;
Ort::Env env_;
Ort::SessionOptions sess_opts_;
Ort::AllocatorWithDefaultOptions allocator_;
std::unique_ptr<Ort::Session> sess_;
std::vector<std::string> input_names_;
std::vector<const char *> input_names_ptr_;
std::vector<std::string> output_names_;
std::vector<const char *> output_names_ptr_;
int32_t vocab_size_ = 0;
};
OfflineTdnnCtcModel::OfflineTdnnCtcModel(const OfflineModelConfig &config)
: impl_(std::make_unique<Impl>(config)) {}
OfflineTdnnCtcModel::~OfflineTdnnCtcModel() = default;
std::pair<Ort::Value, Ort::Value> OfflineTdnnCtcModel::Forward(
Ort::Value features, Ort::Value /*features_length*/) {
return impl_->Forward(std::move(features));
}
int32_t OfflineTdnnCtcModel::VocabSize() const { return impl_->VocabSize(); }
OrtAllocator *OfflineTdnnCtcModel::Allocator() const {
return impl_->Allocator();
}
} // namespace sherpa_onnx
... ...
// sherpa-onnx/csrc/offline-tdnn-ctc-model.h
//
// Copyright (c) 2023 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_TDNN_CTC_MODEL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_TDNN_CTC_MODEL_H_
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/offline-ctc-model.h"
#include "sherpa-onnx/csrc/offline-model-config.h"
namespace sherpa_onnx {
/** This class implements the tdnn model of the yesno recipe from icefall.
*
* See
* https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR/tdnn
*/
class OfflineTdnnCtcModel : public OfflineCtcModel {
public:
explicit OfflineTdnnCtcModel(const OfflineModelConfig &config);
~OfflineTdnnCtcModel() override;
/** Run the forward method of the model.
*
* @param features A tensor of shape (N, T, C). It is changed in-place.
* @param features_length A 1-D tensor of shape (N,) containing number of
* valid frames in `features` before padding.
* Its dtype is int64_t.
*
* @return Return a pair containing:
* - log_probs: A 3-D tensor of shape (N, T', vocab_size).
* - log_probs_length A 1-D tensor of shape (N,). Its dtype is int64_t
*/
std::pair<Ort::Value, Ort::Value> Forward(
Ort::Value features, Ort::Value /*features_length*/) override;
/** Return the vocabulary size of the model
*/
int32_t VocabSize() const override;
/** Return an allocator for allocating memory
*/
OrtAllocator *Allocator() const override;
private:
class Impl;
std::unique_ptr<Impl> impl_;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_TDNN_CTC_MODEL_H_
... ...
// sherpa-onnx/csrc/offline-tdnn-model-config.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/offline-tdnn-model-config.h"
#include "sherpa-onnx/csrc/file-utils.h"
#include "sherpa-onnx/csrc/macros.h"
namespace sherpa_onnx {
void OfflineTdnnModelConfig::Register(ParseOptions *po) {
po->Register("tdnn-model", &model, "Path to onnx model");
}
bool OfflineTdnnModelConfig::Validate() const {
if (!FileExists(model)) {
SHERPA_ONNX_LOGE("tdnn model file %s does not exist", model.c_str());
return false;
}
return true;
}
std::string OfflineTdnnModelConfig::ToString() const {
std::ostringstream os;
os << "OfflineTdnnModelConfig(";
os << "model=\"" << model << "\")";
return os.str();
}
} // namespace sherpa_onnx
... ...
// sherpa-onnx/csrc/offline-tdnn-model-config.h
//
// Copyright (c) 2023 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_TDNN_MODEL_CONFIG_H_
#define SHERPA_ONNX_CSRC_OFFLINE_TDNN_MODEL_CONFIG_H_
#include <string>
#include "sherpa-onnx/csrc/parse-options.h"
namespace sherpa_onnx {
// for https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR/tdnn
struct OfflineTdnnModelConfig {
std::string model;
OfflineTdnnModelConfig() = default;
explicit OfflineTdnnModelConfig(const std::string &model) : model(model) {}
void Register(ParseOptions *po);
bool Validate() const;
std::string ToString() const;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_TDNN_MODEL_CONFIG_H_
... ...
... ... @@ -14,10 +14,14 @@
int main(int32_t argc, char *argv[]) {
const char *kUsageMessage = R"usage(
Speech recognition using non-streaming models with sherpa-onnx.
Usage:
(1) Transducer from icefall
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/index.html
./bin/sherpa-onnx-offline \
--tokens=/path/to/tokens.txt \
--encoder=/path/to/encoder.onnx \
... ... @@ -30,6 +34,8 @@ Usage:
(2) Paraformer from FunASR
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/index.html
./bin/sherpa-onnx-offline \
--tokens=/path/to/tokens.txt \
--paraformer=/path/to/model.onnx \
... ... @@ -39,6 +45,8 @@ Usage:
(3) Whisper models
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html
./bin/sherpa-onnx-offline \
--whisper-encoder=./sherpa-onnx-whisper-base.en/base.en-encoder.int8.onnx \
--whisper-decoder=./sherpa-onnx-whisper-base.en/base.en-decoder.int8.onnx \
... ... @@ -46,6 +54,31 @@ Usage:
--num-threads=1 \
/path/to/foo.wav [bar.wav foobar.wav ...]
(4) NeMo CTC models
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/index.html
./bin/sherpa-onnx-offline \
--tokens=./sherpa-onnx-nemo-ctc-en-conformer-medium/tokens.txt \
--nemo-ctc-model=./sherpa-onnx-nemo-ctc-en-conformer-medium/model.onnx \
--num-threads=2 \
--decoding-method=greedy_search \
--debug=false \
./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/0.wav \
./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/1.wav \
./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/8k.wav
(5) TDNN CTC model for the yesno recipe from icefall
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/yesno/index.html
//
./build/bin/sherpa-onnx-offline \
--sample-rate=8000 \
--feat-dim=23 \
--tokens=./sherpa-onnx-tdnn-yesno/tokens.txt \
--tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav
Note: It supports decoding multiple files in batches
... ...
... ... @@ -10,6 +10,7 @@ pybind11_add_module(_sherpa_onnx
offline-paraformer-model-config.cc
offline-recognizer.cc
offline-stream.cc
offline-tdnn-model-config.cc
offline-transducer-model-config.cc
offline-whisper-model-config.cc
online-lm-config.cc
... ...
... ... @@ -10,6 +10,7 @@
#include "sherpa-onnx/csrc/offline-model-config.h"
#include "sherpa-onnx/python/csrc/offline-nemo-enc-dec-ctc-model-config.h"
#include "sherpa-onnx/python/csrc/offline-paraformer-model-config.h"
#include "sherpa-onnx/python/csrc/offline-tdnn-model-config.h"
#include "sherpa-onnx/python/csrc/offline-transducer-model-config.h"
#include "sherpa-onnx/python/csrc/offline-whisper-model-config.h"
... ... @@ -20,24 +21,28 @@ void PybindOfflineModelConfig(py::module *m) {
PybindOfflineParaformerModelConfig(m);
PybindOfflineNemoEncDecCtcModelConfig(m);
PybindOfflineWhisperModelConfig(m);
PybindOfflineTdnnModelConfig(m);
using PyClass = OfflineModelConfig;
py::class_<PyClass>(*m, "OfflineModelConfig")
.def(py::init<const OfflineTransducerModelConfig &,
const OfflineParaformerModelConfig &,
const OfflineNemoEncDecCtcModelConfig &,
const OfflineWhisperModelConfig &, const std::string &,
const OfflineWhisperModelConfig &,
const OfflineTdnnModelConfig &, const std::string &,
int32_t, bool, const std::string &, const std::string &>(),
py::arg("transducer") = OfflineTransducerModelConfig(),
py::arg("paraformer") = OfflineParaformerModelConfig(),
py::arg("nemo_ctc") = OfflineNemoEncDecCtcModelConfig(),
py::arg("whisper") = OfflineWhisperModelConfig(), py::arg("tokens"),
py::arg("whisper") = OfflineWhisperModelConfig(),
py::arg("tdnn") = OfflineTdnnModelConfig(), py::arg("tokens"),
py::arg("num_threads"), py::arg("debug") = false,
py::arg("provider") = "cpu", py::arg("model_type") = "")
.def_readwrite("transducer", &PyClass::transducer)
.def_readwrite("paraformer", &PyClass::paraformer)
.def_readwrite("nemo_ctc", &PyClass::nemo_ctc)
.def_readwrite("whisper", &PyClass::whisper)
.def_readwrite("tdnn", &PyClass::tdnn)
.def_readwrite("tokens", &PyClass::tokens)
.def_readwrite("num_threads", &PyClass::num_threads)
.def_readwrite("debug", &PyClass::debug)
... ...
// sherpa-onnx/python/csrc/offline-tdnn-model-config.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/offline-tdnn-model-config.h"
#include <string>
#include <vector>
#include "sherpa-onnx/python/csrc/offline-tdnn-model-config.h"
namespace sherpa_onnx {
void PybindOfflineTdnnModelConfig(py::module *m) {
using PyClass = OfflineTdnnModelConfig;
py::class_<PyClass>(*m, "OfflineTdnnModelConfig")
.def(py::init<const std::string &>(), py::arg("model"))
.def_readwrite("model", &PyClass::model)
.def("__str__", &PyClass::ToString);
}
} // namespace sherpa_onnx
... ...
// sherpa-onnx/python/csrc/offline-tdnn-model-config.h
//
// Copyright (c) 2023 Xiaomi Corporation
#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_TDNN_MODEL_CONFIG_H_
#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_TDNN_MODEL_CONFIG_H_
#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
namespace sherpa_onnx {
void PybindOfflineTdnnModelConfig(py::module *m);
}
#endif // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_TDNN_MODEL_CONFIG_H_
... ...
... ... @@ -8,6 +8,7 @@ from _sherpa_onnx import (
OfflineModelConfig,
OfflineNemoEncDecCtcModelConfig,
OfflineParaformerModelConfig,
OfflineTdnnModelConfig,
OfflineWhisperModelConfig,
)
from _sherpa_onnx import OfflineRecognizer as _Recognizer
... ... @@ -37,7 +38,7 @@ class OfflineRecognizer(object):
decoder: str,
joiner: str,
tokens: str,
num_threads: int,
num_threads: int = 1,
sample_rate: int = 16000,
feature_dim: int = 80,
decoding_method: str = "greedy_search",
... ... @@ -48,7 +49,7 @@ class OfflineRecognizer(object):
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/index.html>`_
to download pre-trained models for different languages, e.g., Chinese,
English, etc.
... ... @@ -115,7 +116,7 @@ class OfflineRecognizer(object):
cls,
paraformer: str,
tokens: str,
num_threads: int,
num_threads: int = 1,
sample_rate: int = 16000,
feature_dim: int = 80,
decoding_method: str = "greedy_search",
... ... @@ -124,9 +125,8 @@ class OfflineRecognizer(object):
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
to download pre-trained models for different languages, e.g., Chinese,
English, etc.
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/index.html>`_
to download pre-trained models.
Args:
tokens:
... ... @@ -179,7 +179,7 @@ class OfflineRecognizer(object):
cls,
model: str,
tokens: str,
num_threads: int,
num_threads: int = 1,
sample_rate: int = 16000,
feature_dim: int = 80,
decoding_method: str = "greedy_search",
... ... @@ -188,7 +188,7 @@ class OfflineRecognizer(object):
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/index.html>`_
to download pre-trained models for different languages, e.g., Chinese,
English, etc.
... ... @@ -244,14 +244,14 @@ class OfflineRecognizer(object):
encoder: str,
decoder: str,
tokens: str,
num_threads: int,
num_threads: int = 1,
decoding_method: str = "greedy_search",
debug: bool = False,
provider: str = "cpu",
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/index.html>`_
to download pre-trained models for different kinds of whisper models,
e.g., tiny, tiny.en, base, base.en, etc.
... ... @@ -301,6 +301,69 @@ class OfflineRecognizer(object):
self.config = recognizer_config
return self
@classmethod
def from_tdnn_ctc(
cls,
model: str,
tokens: str,
num_threads: int = 1,
sample_rate: int = 8000,
feature_dim: int = 23,
decoding_method: str = "greedy_search",
debug: bool = False,
provider: str = "cpu",
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/yesno/index.html>`_
to download pre-trained models.
Args:
model:
Path to ``model.onnx``.
tokens:
Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
columns::
symbol integer_id
num_threads:
Number of threads for neural network computation.
sample_rate:
Sample rate of the training data used to train the model.
feature_dim:
Dimension of the feature used to train the model.
decoding_method:
Valid values are greedy_search.
debug:
True to show debug messages.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
"""
self = cls.__new__(cls)
model_config = OfflineModelConfig(
tdnn=OfflineTdnnModelConfig(model=model),
tokens=tokens,
num_threads=num_threads,
debug=debug,
provider=provider,
model_type="tdnn",
)
feat_config = OfflineFeatureExtractorConfig(
sampling_rate=sample_rate,
feature_dim=feature_dim,
)
recognizer_config = OfflineRecognizerConfig(
feat_config=feat_config,
model_config=model_config,
decoding_method=decoding_method,
)
self.recognizer = _Recognizer(recognizer_config)
self.config = recognizer_config
return self
def create_stream(self, contexts_list: Optional[List[List[int]]] = None):
if contexts_list is None:
return self.recognizer.create_stream()
... ...