Add inverse text normalization for non-streaming ASR (#1017)

python-api-examples/inverse-text-normalization-offline-asr.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+#
+# Copyright (c)  2024  Xiaomi Corporation
+
+"""
+This script shows how to use inverse text normalization with non-streaming ASR.
+
+Usage:
+
+(1) Download the test model
+
+wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-paraformer-zh-2023-03-28.tar.bz2
+tar xvf sherpa-onnx-paraformer-zh-2023-03-28.tar.bz2
+rm sherpa-onnx-paraformer-zh-2023-03-28.tar.bz2
+
+(2) Download rule fst
+
+wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/itn_zh_number.fst
+
+Please refer to
+https://github.com/k2-fsa/colab/blob/master/sherpa-onnx/itn_zh_number.ipynb
+for how itn_zh_number.fst is generated.
+
+(3) Download test wave
+
+wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/itn-zh-number.wav
+
+(4) Run this script
+
+python3 ./python-api-examples/inverse-text-normalization-offline-asr.py
+"""
+from pathlib import Path
+
+import sherpa_onnx
+import soundfile as sf
+
+
+def create_recognizer():
+    model = "./sherpa-onnx-paraformer-zh-2023-03-28/model.int8.onnx"
+    tokens = "./sherpa-onnx-paraformer-zh-2023-03-28/tokens.txt"
+    rule_fsts = "./itn_zh_number.fst"
+
+    if (
+        not Path(model).is_file()
+        or not Path(tokens).is_file()
+        or not Path(rule_fsts).is_file()
+    ):
+        raise ValueError(
+            """Please download model files from
+            https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
+            """
+        )
+    return sherpa_onnx.OfflineRecognizer.from_paraformer(
+        paraformer=model,
+        tokens=tokens,
+        debug=True,
+        rule_fsts=rule_fsts,
+    )
+
+
+def main():
+    recognizer = create_recognizer()
+    wave_filename = "./itn-zh-number.wav"
+    if not Path(wave_filename).is_file():
+        raise ValueError(
+            """Please download model files from
+            https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
+            """
+        )
+    audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
+    audio = audio[:, 0]  # only use the first channel
+
+    stream = recognizer.create_stream()
+    stream.accept_waveform(sample_rate, audio)
+    recognizer.decode_stream(stream)
+    print(wave_filename)
+    print(stream.result)
+
+
+if __name__ == "__main__":
+    main()