Add support for Meta Omnilingual ASR v2 models

python-api-examples/offline-omnilingual-asr-ctc-v2-decode-files.py

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+#!/usr/bin/env python3
+
+"""
+This file shows how to use a non-streaming Omnilingual ASR CTC v2 model from
+https://github.com/facebookresearch/omnilingual-asr
+to decode files.
+
+Please download model files from
+https://huggingface.co/Edison2ST/sherpa-onnx-omnilingual-asr-1600-languages-ctc-v2
+
+For instance,
+
+wget https://huggingface.co/Edison2ST/sherpa-onnx-omnilingual-asr-1600-languages-ctc-v2/resolve/main/sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05.tar.bz2 # noqa: E501
+tar xvf sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05.tar.bz2
+rm sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05.tar.bz2
+"""
+
+from pathlib import Path
+
+import numpy as np
+import time
+import sherpa_onnx
+import soundfile as sf
+
+
+def create_recognizer():
+    model = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05/model.int8.onnx"
+    tokens = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05/tokens.txt"
+    test_wav_en = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05/test_wavs/en.wav"
+    test_wav_de = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05/test_wavs/de.wav"
+    test_wav_fr = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05/test_wavs/fr.wav"
+    test_wav_es = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-v2-int8-2026-02-05/test_wavs/es.wav"
+
+    for f in [model, tokens, test_wav_en, test_wav_de, test_wav_fr, test_wav_es]:
+        if not Path(f).is_file():
+            print(f"{f} does not exist")
+
+            raise ValueError("""Please download model files from
+                https://huggingface.co/Edison2ST/sherpa-onnx-omnilingual-asr-1600-languages-ctc-v2
+                """)
+    return (
+        sherpa_onnx.OfflineRecognizer.from_omnilingual_asr_ctc(
+            model=model,
+            tokens=tokens,
+            num_threads=1,
+        ),
+        test_wav_en,
+        test_wav_de,
+        test_wav_fr,
+        test_wav_es,
+    )
+
+
+def load_audio(filename):
+    audio, sample_rate = sf.read(filename, dtype="float32", always_2d=True)
+    audio = audio[:, 0]  # only use the first channel
+    if sample_rate != 16000:
+        import librosa
+
+        audio = librosa.resample(audio, orig_sr=sample_rate, target_sr=16000)
+
+    return np.ascontiguousarray(audio)
+
+
+def decode_single_file(recognizer, filename):
+    samples = load_audio(filename)
+
+    start_time = time.time()
+
+    stream = recognizer.create_stream()
+    stream.accept_waveform(sample_rate=16000, waveform=samples)
+    recognizer.decode_stream(stream)
+
+    end_time = time.time()
+    elapsed_seconds = end_time - start_time
+    audio_duration = len(samples) / 16000
+    real_time_factor = elapsed_seconds / audio_duration
+
+    print("---")
+    print(filename)
+    print(stream.result)
+    print(f"Elapsed seconds: {elapsed_seconds:.3f}")
+    print(f"Audio duration in seconds: {audio_duration:.3f}")
+    print(f"RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {real_time_factor:.3f}")
+    print()
+
+
+def decode_multiple_files(recognizer, filenames):
+    streams = []
+
+    start_time = time.time()
+
+    audio_duration = 0
+
+    for filename in filenames:
+        samples = load_audio(filename)
+        audio_duration += len(samples) / 16000
+
+        stream = recognizer.create_stream()
+        stream.accept_waveform(sample_rate=16000, waveform=samples)
+        streams.append(stream)
+
+    recognizer.decode_streams(streams)
+
+    end_time = time.time()
+    elapsed_seconds = end_time - start_time
+    real_time_factor = elapsed_seconds / audio_duration
+
+    for name, stream in zip(filenames, streams):
+        print("---")
+        print(name)
+        print(stream.result)
+        print()
+
+    print(f"Elapsed seconds: {elapsed_seconds:.3f}")
+    print(f"Audio duration in seconds: {audio_duration:.3f}")
+    print(f"RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {real_time_factor:.3f}")
+    print()
+    print()
+
+
+def main():
+    recognizer, *filenames = create_recognizer()
+
+    decode_single_file(recognizer, filenames[0])
+    decode_single_file(recognizer, filenames[1])
+    decode_multiple_files(recognizer, filenames[2:])
+
+
+if __name__ == "__main__":
+    main()