mirror of
https://github.com/k2-fsa/sherpa-onnx.git
synced 2026-11-10 21:11:03 +08:00
Fangjun Kuang
36ef8d1fbc
This PR adds C++ and Python API support for Omnilingual ASR models with CTC decoding, enabling speech recognition across 1600+ languages using models from Facebook Research's omnilingual-asr project. Key changes: - Implements OfflineOmnilingualAsrCtcModel class with feature normalization and model inference - Adds Python bindings through from_omnilingual_asr_ctc() method on OfflineRecognizer - Integrates omnilingual models into existing CTC recognition pipeline with special handling for input shapes and frame shifts
47 lines
1.5 KiB
Python
Executable File
47 lines
1.5 KiB
Python
Executable File
# Example using the sherpa-onnx Python API and sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8 model
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# Prints recognized text, per-token timestamps, and durations
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import os
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import sys
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import sherpa_onnx
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import soundfile as sf
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wav_filename = "./sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8/test_wavs/en.wav"
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encoder = "./sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8/encoder.int8.onnx"
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decoder = "./sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8/decoder.int8.onnx"
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joiner = "./sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8/joiner.int8.onnx"
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tokens = "./sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8/tokens.txt"
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if not os.path.exists(wav_filename):
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print(f"File not found: {wav_filename}")
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sys.exit(1)
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recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
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encoder,
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decoder,
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joiner,
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tokens,
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num_threads=1,
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provider="cpu",
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debug=False,
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decoding_method="greedy_search",
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model_type="nemo_transducer"
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)
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audio, sample_rate = sf.read(wav_filename, dtype="float32", always_2d=True)
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audio = audio[:, 0] # use first channel if multi-channel
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate, audio)
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recognizer.decode_stream(stream)
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result = stream.result
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print(f"Recognized text: {result.text}")
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if hasattr(result, "tokens") and hasattr(result, "timestamps") and hasattr(result, "durations"):
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print("Token\tTimestamp\tDuration")
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for token, ts, dur in zip(result.tokens, result.timestamps, result.durations):
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print(f"{token}\t{ts:.2f}\t{dur:.2f}")
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else:
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print("Timestamps or durations not available.")
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