Wasser1462
93a0e67d52
This pull request expands the capabilities of sherpa-onnx by integrating offline Qwen3-ASR models. It provides the necessary infrastructure, including model loading, configuration, and a specialized tokenizer, to enable accurate speech recognition for these models. The addition of C, C++, and Python examples ensures ease of adoption for developers looking to leverage Qwen3-ASR in their applications.
File description
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./http_server.py It defines which files to server. Files are saved in ./web.
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non_streaming_server.py WebSocket server for non-streaming models.
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vad-remove-non-speech-segments.py It uses silero-vad to remove non-speech segments and concatenate all speech segments into a single one.
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vad-with-non-streaming-asr.py It shows how to use VAD with a non-streaming ASR model for speech recognition from a microphone
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offline-speech-enhancement-gtcrn.py It shows how to use the offline speech denoiser API with GTCRN.
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offline-speech-enhancement-dpdfnet.py It shows how to use the offline speech denoiser API with DPDFNet.
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online-speech-enhancement-gtcrn.py It shows how to use the online speech denoiser API with GTCRN.
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online-speech-enhancement-dpdfnet.py It shows how to use the online speech denoiser API with DPDFNet. models. Use 16 kHz DPDFNet models such as
dpdfnet_baseline.onnx,dpdfnet2.onnx,dpdfnet4.onnx, ordpdfnet8.onnxfor downstream ASR anddpdfnet2_48khz_hr.onnxfor 48 kHz enhancement output. -
pocket-tts.py It shows how to use PocketTTS with the
GenerationConfigAPI. -
supertonic-tts.py It shows how to use SupertonicTTS with the
GenerationConfigAPI. -
zipvoice-tts.py It shows how to use ZipVoice for zero-shot TTS with the
GenerationConfigAPI. -
zipvoice-tts-play.py It shows how to use ZipVoice for zero-shot TTS and plays the generated audio while it is being synthesized.