Create app.py

app.py

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+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from datasets import load_dataset
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "openai/whisper-large-v3-turbo"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+# dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
+# sample = dataset[0]["audio"]
+# result = pipe(sample)
+# transcript = result["text"]
+
+import os
+import gradio as gr
+
+def launch(input):
+    out = pipe(input)
+    result = pipe(out[0])
+    transcript = result["text"]
+
+    # context_str = out[0]['generated_text']
+    # translate_str = translate(context_str, 'en', 'sq')
+    return translate_str
+
+iface = gr.Interface(launch,
+                     inputs=gr.Audio(label="Audio", source="microphone", type="filepath", elem_id='audio'),
+                     outputs="text")
+
+iface.launch(share=True)
+# iface.launch(share=True, 
+#              server_port=int(os.environ['PORT1']))
+
+iface.close()
+
+
+# def click_js():
+#     return """function audioRecord() {
+#     var xPathRes = document.evaluate ('//*[@id="audio"]//button', document, null, XPathResult.FIRST_ORDERED_NODE_TYPE, null); 
+#     xPathRes.singleNodeValue.click();}"""
+
+
+# def action(btn):
+#     """Changes button text on click"""
+#     if btn == 'Speak': return 'Stop'
+#     else: return 'Speak'
+
+
+# def check_btn(btn):
+#     """Checks for correct button text before invoking transcribe()"""
+#     if btn != 'Speak': raise Exception('Recording...')
+
+
+# def transcribe():
+#     return 'Success'
+
+
+# with gr.Blocks() as demo:
+#     msg = gr.Textbox()
+#     audio_box = gr.Audio(label="Audio", source="microphone", type="filepath", elem_id='audio')
+
+#     with gr.Row():
+#         audio_btn = gr.Button('Speak')
+#         clear = gr.Button("Clear")
+
+#     audio_btn.click(fn=action, inputs=audio_btn, outputs=audio_btn).\
+#               then(fn=lambda: None, _js=click_js()).\
+#               then(fn=check_btn, inputs=audio_btn).\
+#               success(fn=transcribe, outputs=msg)
+
+#     clear.click(lambda: None, None, msg, queue=False)
+
+# demo.queue().launch(debug=True)
+
+# print(result["text"])