Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Huggingface
-emoji: 📊
-colorFrom: pink
-colorTo: pink
+title: huggingface
+app_file: speech.py
 sdk: gradio
-sdk_version: 4.22.0
-app_file: app.py
-pinned: false
+sdk_version: 4.21.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

speech.py

@@ -0,0 +1,55 @@
+#Build a shareable app with Gradio
+
+from transformers import pipeline
+asr = pipeline(task="automatic-speech-recognition",
+               model="distil-whisper/distil-small.en") 
+               
+         
+import os
+import gradio as gr
+
+demo = gr.Blocks()
+
+def transcribe_speech(filepath):
+    if filepath is None:
+        gr.Warning("No audio found, please retry.")
+        return ""
+    output = asr(filepath)
+    return output["text"]
+    
+mic_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(sources="microphone",
+                    type="filepath"),
+    outputs=gr.Textbox(label="Transcription",
+                       lines=3),
+    allow_flagging="never")
+    
+file_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    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, 
+            server_port=int(os.environ.get('PORT1',8080)))
+
+demo.close()
+'''                       
+                       
+import soundfile as sf
+import io
+audio, sampling_rate = sf.read('output.wav')
+print(audio.shape)   
+'''