Create run.py

run.py

@@ -0,0 +1,57 @@
+from deepspeech import Model
+import gradio as gr
+import numpy as np
+import urllib.request
+
+model_file_path = "deepspeech-0.9.3-models.pbmm"
+lm_file_path = "deepspeech-0.9.3-models.scorer"
+url = "https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/"
+
+urllib.request.urlretrieve(url + model_file_path, filename=model_file_path)
+urllib.request.urlretrieve(url + lm_file_path, filename=lm_file_path)
+
+beam_width = 100
+lm_alpha = 0.93
+lm_beta = 1.18
+
+model = Model(model_file_path)
+model.enableExternalScorer(lm_file_path)
+model.setScorerAlphaBeta(lm_alpha, lm_beta)
+model.setBeamWidth(beam_width)
+
+
+def reformat_freq(sr, y):
+    if sr not in (
+        48000,
+        16000,
+    ):  # Deepspeech only supports 16k, (we convert 48k -> 16k)
+        raise ValueError("Unsupported rate", sr)
+    if sr == 48000:
+        y = (
+            ((y / max(np.max(y), 1)) * 32767)
+            .reshape((-1, 3))
+            .mean(axis=1)
+            .astype("int16")
+        )
+        sr = 16000
+    return sr, y
+
+
+def transcribe(speech, stream):
+    _, y = reformat_freq(*speech)
+    if stream is None:
+        stream = model.createStream()
+    stream.feedAudioContent(y)
+    text = stream.intermediateDecode()
+    return text, stream
+
+
+demo = gr.Interface(
+    transcribe,
+    [gr.Audio(source="microphone", streaming=True), "state"],
+    ["text", "state"],
+    live=True,
+)
+
+if __name__ == "__main__":
+    demo.launch()