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from importlib.metadata import version
from timeit import default_timer as timer
import gradio as gr
import numpy as np
import onnx_asr
print(f"onnx_asr version: {version('onnx_asr')}")
models = {
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",
"whisper-base",
]
}
def recognize(audio: tuple[int, np.ndarray]):
sample_rate, waveform = audio
try:
waveform = waveform.astype(np.float32) / 2 ** (8 * waveform.itemsize - 1)
results = []
for name, model in models.items():
start = timer()
result = model.recognize(waveform, sample_rate=sample_rate, language="ru")
time = timer() - start
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
with gr.Blocks() as demo:
gr.Markdown("""
# ASR demo using onnx-asr (Russian models)
**[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`).
""")
input = gr.Audio(min_length=1, max_length=20)
with gr.Row():
gr.ClearButton(input)
btn = gr.Button("Recognize", variant="primary")
output = gr.Dataframe(headers=["model", "result", "time"], wrap=True)
btn.click(fn=recognize, inputs=input, outputs=output)
with gr.Accordion("ASR models used in this demo", open=False):
gr.Markdown("""
* `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))
* `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))
* `whisper-base` - OpenAI Whisper Base exported with onnxruntime ([origin](https://huggingface.co/openai/whisper-base), [onnx](https://huggingface.co/istupakov/whisper-base-onnx))
""")
demo.launch()
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