Update app.py

app.py

@@ -1,64 +1,64 @@
-# from transformers import pipeline
-# import gradio as gr
+from transformers import pipeline
+import gradio as gr
 
-# pipe = pipeline(model="jsbeaudry/jsbeaudry/kreyol-tts")  
+pipe = pipeline(model="jsbeaudry/creole-speech-to-text")  
 
-# def transcribe(audio):
-#     text = pipe(audio)["text"]
-#     return text
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
 
 
-# iface = gr.Interface(
-#     fn=transcribe,
-#     inputs=gr.Audio(type="filepath"), 
-#     outputs="text",
-#     title="Whisper medium Creole",
-#     description="Realtime demo for Haitian Creole speech recognition using a fine-tuned medium small model.",
-# )
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath"), 
+    outputs="text",
+    title="Whisper medium Creole",
+    description="Realtime demo for Haitian Creole speech recognition using a fine-tuned medium small model.",
+)
 
-# iface.launch()
+iface.launch()
 
 
 
-from transformers import pipeline
-import gradio as gr
+# from transformers import pipeline
+# import gradio as gr
 
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-from datasets import load_dataset
+# import torch
+# from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+# from datasets import load_dataset
 
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+# device = "cuda:0" if torch.cuda.is_available() else "cpu"
+# torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
-model_id = "jsbeaudry/creole-speech-to-text"
+# model_id = "jsbeaudry/creole-speech-to-text"
 
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
+# model = AutoModelForSpeechSeq2Seq.from_pretrained(
+#     model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+# )
+# model.to(device)
 
-processor = AutoProcessor.from_pretrained(model_id)
+# processor = AutoProcessor.from_pretrained(model_id)
 
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-def transcribe(audio):
-    # Use the 'whisper' pipeline defined in the previous cell
-    text = pipe(audio)["text"]
-    return text
+# pipe = pipeline(
+#     "automatic-speech-recognition",
+#     model=model,
+#     tokenizer=processor.tokenizer,
+#     feature_extractor=processor.feature_extractor,
+#     torch_dtype=torch_dtype,
+#     device=device,
+# )
+# def transcribe(audio):
+#     # Use the 'whisper' pipeline defined in the previous cell
+#     text = pipe(audio)["text"]
+#     return text
 
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(type="filepath"),
-    outputs="text",
-    title="Whisper medium Creole",
-    description="Realtime demo for Haitian Creole speech recognition using a fine-tuned medium small model.",
-)
+# iface = gr.Interface(
+#     fn=transcribe,
+#     inputs=gr.Audio(type="filepath"),
+#     outputs="text",
+#     title="Whisper medium Creole",
+#     description="Realtime demo for Haitian Creole speech recognition using a fine-tuned medium small model.",
+# )
 
-iface.launch()
+# iface.launch()