Create app.py

app.py

@@ -0,0 +1,52 @@
+from transformers.utils import logging
+logging.set_verbosity_error()
+# from datasets import load_dataset
+from transformers import pipeline
+
+# dataset = load_dataset("librispeech_asr",
+#                        split="train.clean.100",
+#                        streaming=True,
+#                        trust_remote_code=True)
+
+asr = pipeline(task="automatic-speech-recognition",
+               model="openai/whisper-small")
+
+demo = gr.Blocks()
+
+def transcribe_long_form(filepath):
+    if filepath is None:
+        gr.Warning("No audio found, please retry.")
+        return ""
+    output = asr(
+      filepath,
+      max_new_tokens=256,
+      chunk_length_s=30,
+      batch_size=8,
+    )
+    return output["text"]
+
+mic_transcribe = gr.Interface(
+    fn=transcribe_long_form,
+    inputs=gr.Audio(sources="microphone",
+                    type="filepath"),
+    outputs=gr.Textbox(label="Transcription",
+                       lines=3),
+    allow_flagging="never")
+
+file_transcribe = gr.Interface(
+    fn=transcribe_long_form,
+    inputs=gr.Audio(sources="upload",
+                    type="filepath"),
+    outputs=gr.Textbox(label="Transcription",
+                       lines=3),
+    allow_flagging="never",
+)
+
+with demo:
+    gr.TabbedInterface(
+        [mic_transcribe,
+         file_transcribe],
+        ["Transcribe Microphone",
+         "Transcribe Audio File"],
+    )
+demo.launch(share=True)