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@@ -24,6 +24,4 @@ USER user
 
 RUN pip3 install -r requirements.txt
 
-EXPOSE 7860
-
 CMD ["python", "app.py"]

app.py

@@ -1,190 +1,27 @@
-import torch
-
-from transformers import pipeline
-
-import numpy as np
+import os
 import gradio as gr
+from helper import load_image_from_url, render_results_in_image
+from transformers import pipeline
+from transformers.utils import logging
+logging.set_verbosity_error()
 
-def _grab_best_device(use_gpu=False):
-    if torch.cuda.device_count() > 0 and use_gpu:
-        device = "cuda"
-    else:
-        device = "cpu"
-    return device
-
-device = _grab_best_device()
-
-default_model_per_language = {
-    "english": "kakao-enterprise/vits-ljs",
-    "spanish": "facebook/mms-tts-spa",
-}
-
-models_per_language = {
-    "english": [
-        "ylacombe/vits_ljs_midlands_male_monospeaker",
-    ],
-    "spanish": [
-        "ylacombe/mms-spa-finetuned-chilean-monospeaker",       
-    ]
-}
-
-HUB_PATH = "ylacombe/vits_ljs_midlands_male_monospeaker"
-
-
-pipe_dict = {
-    "current_model": "ylacombe/vits_ljs_midlands_male_monospeaker",
-    "pipe":  pipeline("text-to-speech", model=HUB_PATH, device=device),
-    "original_pipe": pipeline("text-to-speech", model=default_model_per_language["english"], device=device),
-    "language": "english",
-}
-
-title =      """
-# Explore MMS finetuning
-## Or how to access truely multilingual TTS
-
-Massively Multilingual Speech (MMS) models are light-weight, low-latency TTS models based on the [VITS architecture](https://huggingface.co/docs/transformers/model_doc/vits).
-
-Meta's [MMS](https://arxiv.org/abs/2305.13516) project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
-and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
-    
-Coupled with the right data and the right training recipe, you can get an excellent finetuned version of every MMS checkpoints in **20 minutes** with as little as **80 to 150 samples**.            
-
-Training recipe available in this [github repository](https://github.com/ylacombe/finetune-hf-vits)!
-            """
-
-max_speakers = 15
-
-
-# Inference
-def generate_audio(text, model_id, language):
-
-    if pipe_dict["language"] != language:
-        gr.Warning(f"Language has changed - loading new default model: {default_model_per_language[language]}")
-        pipe_dict["language"] = language
-        pipe_dict["original_pipe"] = pipeline("text-to-speech", model=default_model_per_language[language], device=device)
-    
-    if pipe_dict["current_model"] != model_id:
-        gr.Warning("Model has changed - loading new model")
-        pipe_dict["pipe"] = pipeline("text-to-speech", model=model_id, device=device)
-        pipe_dict["current_model"] = model_id
-
-    num_speakers = pipe_dict["pipe"].model.config.num_speakers
-
-    out = []
-    # first generate original model result
-    output = pipe_dict["original_pipe"](text)
-    output =  gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label=f"Non finetuned model prediction {default_model_per_language[language]}", show_label=True,
-                               visible=True)
-    out.append(output)
-    
-    
-    if num_speakers>1:
-        for i in range(min(num_speakers, max_speakers - 1)):
-            forward_params = {"speaker_id": i}
-            output = pipe_dict["pipe"](text, forward_params=forward_params)
-            
-            output =  gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label=f"Generated Audio - speaker {i}", show_label=True,
-                               visible=True)
-            out.append(output)
-        out.extend([gr.Audio(visible=False)]*(max_speakers-num_speakers))
-    else:
-        output = pipe_dict["pipe"](text)
-        output =  gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label="Generated Audio - Mono speaker", show_label=True,
-                               visible=True)
-        out.append(output)
-        out.extend([gr.Audio(visible=False)]*(max_speakers-2))
-    return out
-
-
-css = """
-#container{
-    margin: 0 auto;
-    max-width: 80rem;
-}
-#intro{
-    max-width: 100%;
-    text-align: center;
-    margin: 0 auto;
-}
-"""
-# Gradio blocks demo    
-with gr.Blocks(css=css) as demo_blocks:
-    gr.Markdown(title, elem_id="intro")
-
-    with gr.Row():
-        with gr.Column():
-            inp_text = gr.Textbox(label="Input Text", info="What sentence would you like to synthesise?")
-            btn = gr.Button("Generate Audio!")
-            language = gr.Dropdown(
-                default_model_per_language.keys(),
-                value = "spanish",
-                label = "language",
-                info = "Language that you want to test"
-            )
-            
-            model_id = gr.Dropdown(
-                    models_per_language["spanish"],
-                    value="ylacombe/mms-spa-finetuned-chilean-monospeaker", 
-                    label="Model", 
-                    info="Model you want to test",
-                    )
-                
-        with gr.Column():
-            outputs = []
-            for i in range(max_speakers):
-                out_audio = gr.Audio(type="numpy", autoplay=False, label=f"Generated Audio - speaker {i}", show_label=True, visible=False)
-                outputs.append(out_audio)
-
-    with gr.Accordion("Datasets and models details", open=False):
-        gr.Markdown("""
-        
-For each language, we used 100 to 150 samples of a single speaker to finetune the model.
-
-### Spanish
-
-* **Model**: [Spanish MMS TTS](https://huggingface.co/facebook/mms-tts-spa).
-* **Datasets**:
-    - [Chilean Spanish TTS dataset](https://huggingface.co/datasets/ylacombe/google-chilean-spanish).
-
-### English
-
-* **Model**: [VITS-ljs](https://huggingface.co/kakao-enterprise/vits-ljs)
-* **Dataset**: [British Isles Accent](https://huggingface.co/datasets/ylacombe/english_dialects). For each accent, we used 100 to 150 samples of a single speaker to finetune [VITS-ljs](https://huggingface.co/kakao-enterprise/vits-ljs).
-
-                    
-                    """) 
-
-    with gr.Accordion("Run VITS and MMS with transformers", open=False):
-        gr.Markdown(
-            """
-        ```bash
-        pip install transformers
-        ```
-        ```py
-        from transformers import pipeline
-        import scipy
-        pipe = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs", device=0)
-        
-        results = pipe("A cinematic shot of a baby racoon wearing an intricate italian priest robe")
-
-        # write to a wav file
-        scipy.io.wavfile.write("audio_vits.wav", rate=results["sampling_rate"], data=results["audio"].squeeze())
-        ```
-        """
-        )
 
+od_pipe = pipeline("object-detection", "./models/facebook/detr-resnet-50")
 
-    language.change(lambda language: gr.Dropdown(
-                    models_per_language[language],
-                    value=models_per_language[language][0], 
-                    label="Model", 
-                    info="Model you want to test",
-                    ),
-                    language,
-                    model_id
-                   )
+def get_pipeline_prediction(pil_image):
     
-    btn.click(generate_audio, [inp_text, model_id, language], outputs)
+    pipeline_output = od_pipe(pil_image)
     
-
-demo_blocks.queue().launch()
+    processed_image = render_results_in_image(pil_image,
+                                            pipeline_output)
+    return processed_image
+
+demo = gr.Interface(
+  fn=get_pipeline_prediction,
+  inputs=gr.Image(label="Input image", 
+                  type="pil"),
+  outputs=gr.Image(label="Output image with predicted instances",
+                   type="pil")
+)
+
+demo_blocks.queue().launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

@@ -1,9 +1,7 @@
 opencv-python-headless<4.3
 gradio
-torch
-torchaudio
 transformers
-ffmpeg
-librosa
 phonemizer
-py-espeak-ng
+py-espeak-ng
+inflect
+timm