Added sample app functionality

README.md

@@ -1,5 +1,5 @@
 ---
-title: Multipa English To Ipa
+title: Multipa Audio To Ipa
 emoji: 🐨
 colorFrom: red
 colorTo: blue
@@ -9,5 +9,8 @@ app_file: app.py
 pinned: false
 license: mit
 ---
+# About 
+This interactive demo allows you to play with audio to International Phonetic Alphabet transcription models trained in the [multIPA](https://github.com/ginic/multipa) project.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Local Testing
+To use audio models locally, you must manually install ffmpeg and ffprobe, see [this discussion](https://discuss.huggingface.co/t/audio-classification-pipeline-valueerror-ffmpeg-was-not-found-but-is-required-to-load-audio-files-from-filename/16137/8).

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+
+from transformers import pipeline
+
+MODEL_NAME="ctaguchi/wav2vec2-large-xlsr-japlmthufielta-ipa1000-ns"
+#MODEL_NAME="ginic/wav2vec-large-xlsr-en-ipa")
+
+pipe = pipeline(task="automatic-speech-recognition", model=MODEL_NAME)
+
+def predict(audio_in):
+    return pipe(audio_in)["text"]
+
+
+def launch_demo():
+    with gr.Blocks() as demo:
+        gr.Markdown(f"""
+        # Automatic International Phonetic Alphabet Transcription
+        This demo allows you to experiment with producing phonetic transcriptions of uploaded or recorded audio using the model '{MODEL_NAME}'.
+        """)
+        gr.Interface(fn=predict, inputs=gr.Audio(type="filepath"), outputs="text", allow_flagging="never")
+
+    demo.launch()
+
+if __name__ == "__main__":
+    launch_demo()

requirements.txt

@@ -0,0 +1,2 @@
+transformers[torch]
+ffmpeg