Update app.py

app.py

@@ -1,37 +1,37 @@
-import gradio as gr
-from datasets import load_dataset
-import torch
-from transformers import SpeechT5ForSpeechToText, SpeechT5Processor
-
-# Load the English subset of the VoxPopuli dataset
-dataset = load_dataset("VoxPopuli", "en")
-
-# Example function to load audio and transcriptions
-def get_sample(dataset):
-    # Get a random sample from the training set
-    sample = dataset['train'][0]  # You can modify to pick a random sample or any sample index
-    audio_file = sample["audio"]["path"]
-    transcription = sample["sentence"]
-    return audio_file, transcription
-
-# Initialize the SpeechT5 model and processor
-processor = SpeechT5Processor.from_pretrained("facebook/speech_t5_base")
-model = SpeechT5ForSpeechToText.from_pretrained("facebook/speech_t5_base")
-
-# Example Gradio interface function
-def transcribe(audio):
-    # Process the audio and get transcription
-    inputs = processor(audio, return_tensors="pt", sampling_rate=16000)
-    with torch.no_grad():
-        logits = model(**inputs).logits
-    transcription = processor.decode(logits[0], skip_special_tokens=True)
-    return transcription
-
-# Load a sample to check if everything is set up
-audio_file, transcription = get_sample(dataset)
-
-# Set up Gradio interface
-iface = gr.Interface(fn=transcribe, inputs=gr.Audio(source="upload", type="filepath"), outputs="text")
-
-# Launch the interface
-iface.launch()
+import gradio as gr
+from datasets import load_dataset
+import torch
+from transformers import SpeechT5ForSpeechToText, SpeechT5Processor
+
+# Load the English subset of the VoxPopuli dataset
+dataset = load_dataset("voxPopuli", "en")
+
+# Example function to load audio and transcriptions
+def get_sample(dataset):
+    # Get a random sample from the training set
+    sample = dataset['train'][0]  # You can modify to pick a random sample or any sample index
+    audio_file = sample["audio"]["path"]
+    transcription = sample["sentence"]
+    return audio_file, transcription
+
+# Initialize the SpeechT5 model and processor
+processor = SpeechT5Processor.from_pretrained("facebook/speech_t5_base")
+model = SpeechT5ForSpeechToText.from_pretrained("facebook/speech_t5_base")
+
+# Example Gradio interface function
+def transcribe(audio):
+    # Process the audio and get transcription
+    inputs = processor(audio, return_tensors="pt", sampling_rate=16000)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    transcription = processor.decode(logits[0], skip_special_tokens=True)
+    return transcription
+
+# Load a sample to check if everything is set up
+audio_file, transcription = get_sample(dataset)
+
+# Set up Gradio interface
+iface = gr.Interface(fn=transcribe, inputs=gr.Audio(source="upload", type="filepath"), outputs="text")
+
+# Launch the interface
+iface.launch()