import logging from importlib.metadata import version from timeit import default_timer as timer import gradio as gr import numpy as np import onnx_asr logging.basicConfig(format="%(asctime)s %(levelname)s %(message)s", level=logging.WARNING) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) logger.info("onnx_asr version: %s", version("onnx_asr")) vad = onnx_asr.load_vad("silero") whisper = {name: onnx_asr.load_model(name) for name in ["whisper-base"]} models_ru = { name: onnx_asr.load_model(name) for name in [ "gigaam-v2-ctc", "gigaam-v2-rnnt", "nemo-fastconformer-ru-ctc", "nemo-fastconformer-ru-rnnt", "alphacep/vosk-model-ru", "alphacep/vosk-model-small-ru", ] } models_en = { name: onnx_asr.load_model(name, quantization="int8") for name in [ "nemo-parakeet-ctc-0.6b", "nemo-parakeet-rnnt-0.6b", ] } models_vad = models_ru | models_en | whisper def recognize(audio: tuple[int, np.ndarray], models, language): if audio is None: return None sample_rate, waveform = audio logger.debug("recognize: sample_rate %s, waveform.shape %s.", sample_rate, waveform.shape) try: waveform = waveform.astype(np.float32) / 2 ** (8 * waveform.itemsize - 1) if waveform.ndim == 2: waveform = waveform.mean(axis=1) results = [] for name, model in models.items(): start = timer() result = model.recognize(waveform, sample_rate=sample_rate, language=language) time = timer() - start logger.debug("recognized by %s: result '%s', time %.3f s.", name, result, time) results.append([name, result, f"{time:.3f} s."]) except Exception as e: raise gr.Error(f"{e} Audio: sample_rate: {sample_rate}, waveform.shape: {waveform.shape}.") from e else: return results def recognize_ru(audio: tuple[int, np.ndarray]): return recognize(audio, models_ru | whisper, "ru") def recognize_en(audio: tuple[int, np.ndarray]): return recognize(audio, models_en | whisper, "en") def recognize_with_vad(audio: tuple[int, np.ndarray], name: str): if audio is None: return None sample_rate, waveform = audio logger.debug("recognize: sample_rate %s, waveform.shape %s.", sample_rate, waveform.shape) try: waveform = waveform.astype(np.float32) / 2 ** (8 * waveform.itemsize - 1) if waveform.ndim == 2: waveform = waveform.mean(axis=1) model = models_vad[name].with_vad(vad, batch_size=1) results = "" for res in model.recognize(waveform, sample_rate=sample_rate): logger.debug("recognized by %s: result '%s'.", name, res) results += f"[{res.start:5.1f}, {res.end:5.1f}]: {res.text}\n" yield results except Exception as e: raise gr.Error(f"{e} Audio: sample_rate: {sample_rate}, waveform.shape: {waveform.shape}.") from e with gr.Blocks() as recognize_short: audio = gr.Audio(min_length=1, max_length=20) with gr.Row(): gr.ClearButton(audio) btn_ru = gr.Button("Recognize (ru)", variant="primary") btn_en = gr.Button("Recognize (en)", variant="primary") output = gr.Dataframe(headers=["model", "result", "time"], wrap=True) btn_ru.click(fn=recognize_ru, inputs=audio, outputs=output) btn_en.click(fn=recognize_en, inputs=audio, outputs=output) with gr.Blocks() as recognize_long: name = gr.Dropdown(models_vad.keys(), label="Model") audio = gr.Audio(min_length=1, max_length=300) with gr.Row(): gr.ClearButton(audio) btn = gr.Button("Recognize", variant="primary") output = gr.TextArea(label="result") # headers=["start", "end", "result"], wrap=True, every=0.1) btn.click(fn=recognize_with_vad, inputs=[audio, name], outputs=output) with gr.Blocks() as demo: gr.Markdown(""" # ASR demo using onnx-asr **[onnx-asr](https://github.com/istupakov/onnx-asr)** is a Python package for Automatic Speech Recognition using ONNX models. The package is written in pure Python with minimal dependencies (no `pytorch` or `transformers`). """) gr.TabbedInterface( [recognize_short, recognize_long], [ "Recognition of a short phrase (up to 20 sec.)", "Recognition of a long phrase with VAD (up to 5 min.)", ], ) with gr.Accordion("Models used in this demo...", open=False): gr.Markdown(""" ## ASR models * `gigaam-v2-ctc` - Sber GigaAM v2 CTC ([origin](https://github.com/salute-developers/GigaAM), [onnx](https://huggingface.co/istupakov/gigaam-v2-onnx)) * `gigaam-v2-rnnt` - Sber GigaAM v2 RNN-T ([origin](https://github.com/salute-developers/GigaAM), [onnx](https://huggingface.co/istupakov/gigaam-v2-onnx)) * `nemo-fastconformer-ru-ctc` - Nvidia FastConformer-Hybrid Large (ru) with CTC decoder ([origin](https://huggingface.co/nvidia/stt_ru_fastconformer_hybrid_large_pc), [onnx](https://huggingface.co/istupakov/stt_ru_fastconformer_hybrid_large_pc_onnx)) * `nemo-fastconformer-ru-rnnt` - Nvidia FastConformer-Hybrid Large (ru) with RNN-T decoder ([origin](https://huggingface.co/nvidia/stt_ru_fastconformer_hybrid_large_pc), [onnx](https://huggingface.co/istupakov/stt_ru_fastconformer_hybrid_large_pc_onnx)) * `nemo-parakeet-ctc-0.6b` - Nvidia Parakeet CTC 0.6B (en) ([origin](https://huggingface.co/nvidia/parakeet-ctc-0.6b), [onnx](https://huggingface.co/istupakov/parakeet-ctc-0.6b-onnx)) * `nemo-parakeet-rnnt-0.6b` - Nvidia Parakeet RNNT 0.6B (en) ([origin](https://huggingface.co/nvidia/parakeet-rnnt-0.6b), [onnx](https://huggingface.co/istupakov/parakeet-rnnt-0.6b-onnx)) * `whisper-base` - OpenAI Whisper Base exported with onnxruntime ([origin](https://huggingface.co/openai/whisper-base), [onnx](https://huggingface.co/istupakov/whisper-base-onnx)) * `alphacep/vosk-model-ru` - Alpha Cephei Vosk 0.54-ru ([origin](https://huggingface.co/alphacep/vosk-model-ru)) * `alphacep/vosk-model-small-ru` - Alpha Cephei Vosk 0.52-small-ru ([origin](https://huggingface.co/alphacep/vosk-model-small-ru)) ## VAD models * `silero` - Silero VAD ([origin](https://github.com/snakers4/silero-vad), [onnx](https://huggingface.co/onnx-community/silero-vad)) """) demo.launch()