Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+import torch
+from faster_whisper import WhisperModel
+import pandas as pd
+
+model_size = "large-v2"
+
+# get device
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+if device == "cuda:0":
+    # Run on GPU with FP16
+    model_whisper = WhisperModel(model_size, device="cuda", compute_type="float16")
+    # or Run on GPU with INT8
+    # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
+else:
+    # Run on CPU with INT8
+    model_whisper = WhisperModel(model_size, device="cpu", compute_type="int8")
+
+def get_filename(file_obj):
+    return file_obj.name.split("/")[-1]
+
+def audio_to_transcript(file_obj):
+    # get all audio segments
+    segments, _ = model_whisper.transcribe(file_obj.name, beam_size=5, vad_filter=True)
+    print("start")
+    start_segments, end_segments, text_segments = list(), list(), list()
+    for segment in segments:
+        start, end, text = segment.start, segment.end, segment.text
+        start_segments.append(start)
+        end_segments.append(end)
+        text_segments.append(text)
+
+    # save transcript into csv
+    df = pd.DataFrame()
+    df["start"] = start_segments
+    df["end"] = end_segments
+    df["text"] = text_segments
+
+    print(df)
+
+    return get_filename(file_obj), df
+
+## Gradio interface
+headers = ["start", "end", "text"]
+iface = gr.Interface(fn=audio_to_transcript,
+                     inputs=gr.File(label="Audio file"),
+                     outputs=[
+                        gr.Textbox(label="Name of the audio file"),
+                        gr.DataFrame(label="Transcript", headers=headers),
+                     ],
+                     allow_flagging="never",
+                     title="Audio to Transcript",
+                     description="Just paste any audio file and get its corresponding transcript with timeline.",
+                     )
+iface.launch()