Update app.py

app.py

@@ -12,7 +12,11 @@ logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 logger.info("onnx_asr version: %s", version("onnx_asr"))
 
-models = {
+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",
@@ -21,12 +25,21 @@ models = {
         "nemo-fastconformer-ru-rnnt",
         "alphacep/vosk-model-ru",
         "alphacep/vosk-model-small-ru",
-        "whisper-base",
     ]
 }
 
+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]):
+def recognize(audio: tuple[int, np.ndarray], models, language):
     if audio is None:
         return None
 
@@ -40,7 +53,7 @@ def recognize(audio: tuple[int, np.ndarray]):
         results = []
         for name, model in models.items():
             start = timer()
-            result = model.recognize(waveform, sample_rate=sample_rate, language="ru")
+            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."])
@@ -50,27 +63,83 @@ def recognize(audio: tuple[int, np.ndarray]):
         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)
+        results = []
+        for res in model.recognize(waveform, sample_rate=sample_rate):
+            logger.debug("recognized by %s: result '%s'.", name, res)
+            results.append([res.start, res.end, res.text])
+
+    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 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.Dataframe(headers=["start", "end", "result"], wrap=True)
+    btn.click(fn=recognize_with_vad, inputs=[audio, name], outputs=output)
+
 with gr.Blocks() as demo:
     gr.Markdown("""
-    # ASR demo using onnx-asr (Russian models)
+    # 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`).
     """)
-    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.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))
-        * `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()