Create app.py

app.py

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+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()