test fix

app.py

@@ -1,29 +1,35 @@
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import gradio as gr
-import librosa 
-
-# load model and processor
-processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
-model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
- 
-# define prediction function
-def audio2phoneme(audio_path):
-  audio, sr = librosa.load(audio_path, sr=16000)
-  input_values = processor(audio, return_tensors="pt", padding=True).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 ' '.join(transcription)
-
-app = gr.Interface(
-    fn=audio2phoneme,
-    inputs=gr.Audio(sources=["upload","microphone"], type="filepath"),
-    outputs=gr.Textbox(label="Phoneme Transcription", show_copy_button=True, show_label=True),
-    description="Get phonemes from audio",
-    title="Audio to Phoneme Transcription using facebook/wav2vec2-lv-60-espeak-cv",
-    )
-
-# start space
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import torch
+import gradio as gr
+import librosa 
+import os
+import subprocess
+
+# Install system dependencies
+subprocess.run(["apt-get", "update"], check=True)
+subprocess.run(["apt-get", "install", "-y", "espeak"], check=True)
+
+# load model and processor
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
+ 
+# define prediction function
+def audio2phoneme(audio_path):
+  audio, sr = librosa.load(audio_path, sr=16000)
+  input_values = processor(audio, return_tensors="pt", padding=True).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 ' '.join(transcription)
+
+app = gr.Interface(
+    fn=audio2phoneme,
+    inputs=gr.Audio(sources=["upload","microphone"], type="filepath"),
+    outputs=gr.Textbox(label="Phoneme Transcription", show_copy_button=True, show_label=True),
+    description="Get phonemes from audio",
+    title="Audio to Phoneme Transcription using facebook/wav2vec2-lv-60-espeak-cv",
+    )
+
+# start space
 app.launch(share=True)