t :# 请为您的变更输入提交说明。以 '#' 开始的行将被忽略，而一个空的提交

backend/gradio_webrtc/speech_to_text/__init__.py

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+from .stt_ import get_stt_model, stt, stt_for_chunks
+
+__all__ = ["stt", "stt_for_chunks", "get_stt_model"]

backend/gradio_webrtc/speech_to_text/stt_.py

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+from dataclasses import dataclass
+from functools import lru_cache
+from typing import Callable
+
+import numpy as np
+from numpy.typing import NDArray
+
+from ..utils import AudioChunk
+
+
+@dataclass
+class STTModel:
+    encoder: Callable
+    decoder: Callable
+
+
+@lru_cache
+def get_stt_model() -> STTModel:
+    from silero import silero_stt
+
+    model, decoder, _ = silero_stt(language="en", version="v6", jit_model="jit_xlarge")
+    return STTModel(model, decoder)
+
+
+def stt(audio: tuple[int, NDArray[np.int16]]) -> str:
+    model = get_stt_model()
+    sr, audio_np = audio
+    if audio_np.dtype != np.float32:
+        print("converting")
+        audio_np = audio_np.astype(np.float32) / 32768.0
+    try:
+        import torch
+    except ImportError:
+        raise ImportError(
+            "PyTorch is required to run speech-to-text for stopword detection. Run `pip install torch`."
+        )
+    audio_torch = torch.tensor(audio_np, dtype=torch.float32)
+    if audio_torch.ndim == 1:
+        audio_torch = audio_torch.unsqueeze(0)
+    assert audio_torch.ndim == 2, "Audio must have a batch dimension"
+    print("before")
+    res = model.decoder(model.encoder(audio_torch)[0])
+    print("after")
+    return res
+
+
+def stt_for_chunks(
+    audio: tuple[int, NDArray[np.int16]], chunks: list[AudioChunk]
+) -> str:
+    sr, audio_np = audio
+    return " ".join(
+        [stt((sr, audio_np[chunk["start"] : chunk["end"]])) for chunk in chunks]
+    )