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.gitignore

@@ -0,0 +1 @@
+.DS_Store

README.md

@@ -1,13 +1,6 @@
 ---
-title: Glaswegian Whisper
-emoji: 😻
-colorFrom: indigo
-colorTo: blue
+title: glaswegian-whisper
+app_file: app.py
 sdk: gradio
 sdk_version: 4.36.1
-app_file: app.py
-pinned: false
-license: apache-2.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -4,11 +4,12 @@ import gradio as gr
 pipe = pipeline(model="divakaivan/whisper-small-hi_test")
 
 def transcribe(audio):
-
+    print(audio)
+    print(type(audio))
     text = pipe(audio)["text"]
     return text
 
-demo = gr.Interface(
+iface = gr.Interface(
     fn=transcribe,
     inputs=gr.Audio(type="filepath"),
     outputs="text",
@@ -16,4 +17,4 @@ demo = gr.Interface(
     description="Realtime demo for Glaswegian speech recognition using a fine-tuned Whisper small model.",
 )
 
-demo.launch(share=True)
+iface.launch(share=True)

app_old.py

@@ -0,0 +1,29 @@
+import streamlit as st
+from st_audiorec import st_audiorec
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+
+processor = AutoProcessor.from_pretrained("openai/whisper-small")
+model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small")
+
+def transcribe(audio):
+
+    text = processor.batch_decode(model.generate(processor(audio), num_beams=4), skip_special_tokens=True)
+    return text
+
+wav_audio_data = st_audiorec()
+
+if wav_audio_data is not None:
+    # st.audio(wav_audio_data, format='audio/wav')
+    st.write("Transcription:")
+    st.write(transcribe(wav_audio_data))
+
+# Set up the Streamlit app
+st.title("Glaswegian Transcription with Whisper")
+api_key = st.sidebar.text_input("Enter your API key")
+
+# Check if API key is provided
+if api_key:
+    st.write("API key:", api_key)
+    # Add your code here to use the Whisper model for audio transcription
+else:
+    st.warning("Please enter your API key in the sidebar.")

requirements.txt

@@ -1,3 +1,3 @@
 transformers
 torch
-torchvision
+torchvision