| 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") |
|
|
| models_multilang = {name: onnx_asr.load_model(name) for name in ["whisper-base"]} | { |
| name: onnx_asr.load_model(name, quantization="int8") for name in ["nemo-parakeet-tdt-0.6b-v3", "nemo-canary-1b-v2"] |
| } |
|
|
| models_ru = { |
| name: onnx_asr.load_model(name) |
| for name in [ |
| "gigaam-v3-ctc", |
| "gigaam-v3-rnnt", |
| "gigaam-v3-e2e-ctc", |
| "gigaam-v3-e2e-rnnt", |
| "nemo-fastconformer-ru-ctc", |
| "nemo-fastconformer-ru-rnnt", |
| "alphacep/vosk-model-ru", |
| "alphacep/vosk-model-small-ru", |
| "t-tech/t-one", |
| ] |
| } |
|
|
| models_en = { |
| name: onnx_asr.load_model(name, quantization="int8") |
| for name in [ |
| "nemo-parakeet-tdt-0.6b-v2", |
| ] |
| } |
|
|
| models_vad = models_multilang | models_ru | models_en |
|
|
|
|
| def recognize(audio: tuple[int, np.ndarray], models, language: str): |
| if audio is None: |
| return None |
|
|
| valid_res = gr.validators.is_audio_correct_length(audio, min_length=1, max_length=30) |
| if not valid_res["is_valid"]: |
| raise gr.Error(valid_res["message"]) |
|
|
| sample_rate, waveform = audio |
| length = waveform.shape[0] / sample_rate |
| logger.debug("recognize: length %.3f, sample_rate %s, waveform.shape %s.", length, 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(): |
| if length > 20 and name == "alphacep/vosk-model-small-ru": |
| gr.Warning(f"Model {name} only supports audio no longer than 20 s.") |
| continue |
|
|
| 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]) |
|
|
| 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 | models_multilang, "ru") |
|
|
|
|
| def recognize_en(audio: tuple[int, np.ndarray]): |
| return recognize(audio, models_en | models_multilang, "en") |
|
|
|
|
| def recognize_with_vad(audio: tuple[int, np.ndarray], name: str): |
| if audio is None: |
| return None |
|
|
| valid_res = gr.validators.is_audio_correct_length(audio, min_length=1, max_length=600) |
| if not valid_res["is_valid"]: |
| raise gr.Error(valid_res["message"]) |
|
|
| sample_rate, waveform = audio |
| length = waveform.shape[0] / sample_rate |
| logger.debug("recognize: length %.3f, sample_rate %s, waveform.shape %s.", length, 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() |
| with gr.Row(): |
| btn_ru = gr.Button("Recognize (ru)", variant="primary") |
| btn_en = gr.Button("Recognize (en)", variant="primary") |
| output = gr.Dataframe(headers=["model", "result"], 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: |
| gr.Markdown("The default VAD parameters are used. For best results, you should adjust the VAD parameters in your app.") |
| name = gr.Dropdown(sorted(models_vad.keys()), value="nemo-parakeet-tdt-0.6b-v3", label="Model") |
| audio = gr.Audio() |
| with gr.Row(): |
| btn = gr.Button("Recognize", variant="primary") |
| output = gr.TextArea(label="result") |
| btn.click(fn=recognize_with_vad, inputs=[audio, name], outputs=output) |
|
|
| def on_model_change(name: str): |
| if name in models_ru: |
| label = f"Model {name} support only Russian language" |
| elif name in models_en: |
| label = f"Model {name} support only English language" |
| else: |
| label = None |
| return gr.Audio(label=label) |
|
|
| name.change(on_model_change, inputs=name, outputs=audio) |
|
|
| 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. |
| It's a lightweight, fast, and easy-to-use pure Python package with minimal dependencies - no need for PyTorch, Transformers, or FFmpeg. |
| |
| Supports **Parakeet v2 (En) / v3 (Multilingual)**, **Canary v2 (Multilingual)** and **GigaAM v2/v3 (Ru)** models |
| (and many other modern [models](https://github.com/istupakov/onnx-asr?tab=readme-ov-file#supported-model-names)). |
| You can also use it with your own model if it has a supported architecture. |
| """) |
| gr.TabbedInterface( |
| [recognize_short, recognize_long], |
| [ |
| "Simple recognition (up to 30 sec.)", |
| "Recognition with VAD (up to 10 min.)", |
| ], |
| ) |
| with gr.Accordion("Models used in this demo:", open=False): |
| gr.Markdown(""" |
| ## Russian ASR models |
| * `gigaam-v3-ctc` - Sber GigaAM v3 CTC ([origin](https://huggingface.co/ai-sage/GigaAM-v3), [onnx](https://huggingface.co/istupakov/gigaam-v3-onnx)) |
| * `gigaam-v3-rnnt` - Sber GigaAM v3 RNN-T ([origin](https://huggingface.co/ai-sage/GigaAM-v3), [onnx](https://huggingface.co/istupakov/gigaam-v3-onnx)) |
| * `gigaam-v3-e2e-ctc` - Sber GigaAM v3 E2E CTC ([origin](https://huggingface.co/ai-sage/GigaAM-v3), [onnx](https://huggingface.co/istupakov/gigaam-v3-onnx)) |
| * `gigaam-v3-e2e-rnnt` - Sber GigaAM v3 E2E RNN-T ([origin](https://huggingface.co/ai-sage/GigaAM-v3), [onnx](https://huggingface.co/istupakov/gigaam-v3-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-tdt-0.6b-v3` - Nvidia Parakeet TDT 0.6B v3 (multilingual) ([origin](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3), [onnx](https://huggingface.co/istupakov/parakeet-tdt-0.6b-v3-onnx)) |
| * `nemo-canary-1b-v2` - Nvidia Canary 1B v2 (multilingual) ([origin](https://huggingface.co/nvidia/canary-1b-v2), [onnx](https://huggingface.co/istupakov/canary-1b-v2-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)) |
| * `t-tech/t-one` - T-Tech T-one ([origin](https://huggingface.co/t-tech/T-one)) |
| ## English ASR models |
| * `nemo-parakeet-tdt-0.6b-v2` - Nvidia Parakeet TDT 0.6B v2 (en) ([origin](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2), [onnx](https://huggingface.co/istupakov/parakeet-tdt-0.6b-v2-onnx)) |
| * `nemo-parakeet-tdt-0.6b-v3` - Nvidia Parakeet TDT 0.6B v3 (multilingual) ([origin](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3), [onnx](https://huggingface.co/istupakov/parakeet-tdt-0.6b-v3-onnx)) |
| * `nemo-canary-1b-v2` - Nvidia Canary 1B v2 (multilingual) ([origin](https://huggingface.co/nvidia/canary-1b-v2), [onnx](https://huggingface.co/istupakov/canary-1b-v2-onnx)) |
| * `whisper-base` - OpenAI Whisper Base exported with onnxruntime ([origin](https://huggingface.co/openai/whisper-base), [onnx](https://huggingface.co/istupakov/whisper-base-onnx)) |
| ## VAD models |
| * `silero` - Silero VAD ([origin](https://github.com/snakers4/silero-vad), [onnx](https://huggingface.co/onnx-community/silero-vad)) |
| """) |
|
|
| demo.launch() |
|
|
