Fix Python APIs

python-api-examples/non_streaming_server.py

@@ -71,6 +71,20 @@
   --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")
 

python-api-examples/offline-decode-files.py

@@ -8,6 +8,7 @@
 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 @@
       /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 @@
   ./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
@@ -159,6 +172,13 @@ def get_args():
         help="Path to the model.onnx from NeMo CTC",
     )
 
+    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,
@@ -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,
         )

python-api-examples/web/js/upload.js

@@ -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);
 }

sherpa-onnx/python/sherpa_onnx/offline_recognizer.py

@@ -8,6 +8,7 @@
     OfflineModelConfig,
     OfflineNemoEncDecCtcModelConfig,
     OfflineParaformerModelConfig,
+    OfflineTdnnModelConfig,
     OfflineWhisperModelConfig,
 )
 from _sherpa_onnx import OfflineRecognizer as _Recognizer
@@ -37,7 +38,7 @@ def from_transducer(
         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 @@ def from_transducer(
     ):
         """
         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 @@ def from_paraformer(
         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 @@ def from_paraformer(
     ):
         """
         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 @@ def from_nemo_ctc(
         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 @@ def from_nemo_ctc(
     ):
         """
         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 @@ def from_whisper(
         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 @@ def from_whisper(
         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()