app

app.py

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+import whisper
+import gradio as gr
+import time
+import os
+
+'''model = whisper.load_model("base")
+print(model.device)'''
+
+
+def speechtotext(tmp_filename, uploaded):
+    try:
+        source = uploaded if uploaded is not None else tmp_filename
+        result = os.system("whisper" + source + " --language Hindi " + " --task translate ")
+        return f'Detected language: {Language.make(language=result["language"]).display_name()}\n\n ' \
+               f'You said: {result["text"]}'
+    except:
+        return "Unable to generate translation"
+
+
+gr.Interface(
+
+    title="NS-AI-Labs Custom Whisper",
+    thumbnail="https://cdn.openai.com/whisper/asr-summary-of-model-architecture-desktop.svg",
+    css="""
+    .gr-prose p{text-align: center;}
+    .gr-button {background: black;color: white}
+    """,
+    description="we customised whisper with some additional ASR layers , speak in any language we are here to get it "
+                "recognised !",
+    fn=speechtotext,
+    inputs=[
+        gr.Audio(label="Record your voice on your mic", source="microphone", type="filepath"),
+        gr.Audio(source="upload", type="filepath", label="Upload Audio")],
+    outputs="text").launch()

requirements.txt

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+langcodes[data]
+git+https://github.com/openai/whisper.git