init2

app.py

@@ -1,4 +1,49 @@
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from diffusers import DiffusionPipeline
+import torch
+import accelerate
+
+# Load the models and tokenizers
+translation_model_name = "google/madlad400-3b-mt"
+translation_model = AutoModelForSeq2SeqLM.from_pretrained(translation_model_name)
+translation_tokenizer = AutoTokenizer.from_pretrained(translation_model_name)
+
+transcription_model = "chrisjay/fonxlsr"
+
+diffusion_model_name = "stabilityai/stable-diffusion-xl-base-1.0"
+diffusion_pipeline = DiffusionPipeline.from_pretrained(diffusion_model_name, torch_dtype=torch.float16)
+diffusion_pipeline = diffusion_pipeline.to("cuda")
+
+# Define the translation and transcription pipeline with accelerate
+translation_pipeline = pipeline("translation", model=translation_model, tokenizer=translation_tokenizer, device_map="auto")
+transcription_pipeline = pipeline("automatic-speech-recognition", model=transcription_model, device_map="auto")
+
+# Define the function for transcribing and translating audio in Fon
+def transcribe_and_translate_audio_fon(audio_path, num_images=1):
+    # Transcribe the audio to Fon using the transcription pipeline
+    transcription_fon = transcription_pipeline(audio_path)["text"]
+
+    # Translate the Fon transcription to French using the translation pipeline
+    translation_result = translation_pipeline(transcription_fon, source_lang="fon", target_lang="fr")
+    translation_fr = translation_result[0]["translation_text"]
+
+    images = diffusion_pipeline(translation_fr, num_images_per_prompt=num_images)["images"]
+
+    return images
+
+# Create a Streamlit app
+st.title("Fon Audio to Image Translation")
+
+# Upload audio file
+audio_file = st.file_uploader("Upload an audio file", type=["wav"])
+
+# Transcribe, translate and generate images
+if audio_file:
+    images = transcribe_and_translate_audio_fon(audio_file)
+    st.image(images[0])
+
+
+# Use Accelerate to distribute the computation across available GPUs
+#images = accelerate.launch(transcribe_and_translate_and_generate, audio_file="Fongbe_Speech_Dataset/Fongbe_Speech_Dataset/fongbe_speech_audio_files/wav/64_fongbe_6b36d45b77344caeb1c8d773303c9dcb_for_validation_2022-03-11-23-50-13.wav", num_images=2)
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)