Add Python API (#31)

sherpa-onnx/python/sherpa_onnx/online_recognizer.py

@@ -0,0 +1,96 @@
+from pathlib import Path
+from typing import List
+
+from _sherpa_onnx import (
+    OnlineStream,
+    OnlineTransducerModelConfig,
+    FeatureExtractorConfig,
+    OnlineRecognizerConfig,
+)
+from _sherpa_onnx import OnlineRecognizer as _Recognizer
+
+
+def _assert_file_exists(f: str):
+    assert Path(f).is_file(), f"{f} does not exist"
+
+
+class OnlineRecognizer(object):
+    """A class for streaming speech recognition."""
+
+    def __init__(
+        self,
+        tokens: str,
+        encoder: str,
+        decoder: str,
+        joiner: str,
+        num_threads: int = 4,
+        sample_rate: float = 16000,
+        feature_dim: int = 80,
+    ):
+        """
+        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.
+
+        Args:
+          tokens:
+            Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
+            columns::
+
+                symbol integer_id
+
+          encoder:
+            Path to ``encoder.onnx``.
+          decoder:
+            Path to ``decoder.onnx``.
+          joiner:
+            Path to ``joiner.onnx``.
+          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.
+        """
+        _assert_file_exists(tokens)
+        _assert_file_exists(encoder)
+        _assert_file_exists(decoder)
+        _assert_file_exists(joiner)
+
+        assert num_threads > 0, num_threads
+
+        model_config = OnlineTransducerModelConfig(
+            encoder_filename=encoder,
+            decoder_filename=decoder,
+            joiner_filename=joiner,
+            num_threads=num_threads,
+        )
+
+        feat_config = FeatureExtractorConfig(
+            sampling_rate=sample_rate,
+            feature_dim=feature_dim,
+        )
+
+        recognizer_config = OnlineRecognizerConfig(
+            feat_config=feat_config,
+            model_config=model_config,
+            tokens=tokens,
+        )
+
+        self.recognizer = _Recognizer(recognizer_config)
+
+    def create_stream(self):
+        return self.recognizer.create_stream()
+
+    def decode_stream(self, s: OnlineStream):
+        self.recognizer.decode_stream(s)
+
+    def decode_streams(self, ss: List[OnlineStream]):
+        self.recognizer.decode_streams(ss)
+
+    def is_ready(self, s: OnlineStream) -> bool:
+        return self.recognizer.is_ready(s)
+
+    def get_result(self, s: OnlineStream) -> str:
+        return self.recognizer.get_result(s).text