Add application file

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import torch
+import phonemizer
+import librosa
+import base64
+
+
+def lark(audioAsB64):
+    # convert b64 audio to wav
+    with open("audio.wav", "wb") as preWaveform:
+        preWaveform.write(base64.b64encode())
+
+    # processing
+    processor = Wav2Vec2Processor.from_pretrained(
+        "facebook/wav2vec2-xlsr-53-espeak-cv-ft"
+    )
+    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
+
+    waveform, sample_rate = librosa.load(
+        "harvard.wav", sr=16000
+    )  # Downsample 44.1kHz to 8kHz
+
+    input_values = processor(
+        waveform, sampling_rate=sample_rate, return_tensors="pt"
+    ).input_values
+
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+
+    return transcription
+
+
+iface = gr.Interface(fn=lark, inputs="text", outputs="text")
+iface.launch()