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app.py

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+from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
+import torch
+from PIL import Image
+
+import gradio as gr 
+
+from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
+from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
+
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
+    "facebook/fastspeech2-en-ljspeech",
+    arg_overrides={"vocoder": "hifigan", "fp16": False}
+)
+model1 = models[0]
+TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
+generator = task.build_generator(models, cfg)
+
+max_length = 16
+num_beams = 4
+gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
+
+
+def predict_step(image_paths):
+  images = []
+  text = ""
+  
+  for image_path in image_paths:
+    i_image = Image.fromarray(image_path)
+    if i_image.mode != "RGB":
+      i_image = i_image.convert(mode="RGB")
+      print(image_path)
+
+    images.append(i_image)
+    print(images)
+  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
+  pixel_values = pixel_values.to(device)
+
+  output_ids = model.generate(pixel_values, **gen_kwargs)
+
+  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+  preds = [pred.strip() for pred in preds]
+  preds = ' '.join(str(e) for e in preds)
+  text = text + preds
+  sample = TTSHubInterface.get_model_input(task, text)
+  wav, rate = TTSHubInterface.get_prediction(task, model1, generator, sample)
+  return wav#, rate, text
+  #return ipd.Audio(wav, rate=rate)
+
+
+interface = gr.Interface(predict_step, gr.Image(), "audio")
+interface.launch()

requirements.txt

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+torch
+fairseq
+gradio
+transformers
+Pillow
+g2p-en