Update app.py

made marathi the default language

app.py

@@ -1,3 +1,192 @@
 import gradio as gr
 
-gr.load("models/facebook/mms-tts-mar").launch()
+gr.load("models/facebook/mms-tts-mar").launch()
+
+
+import torch
+
+from transformers import pipeline
+
+import numpy as np
+import gradio as gr
+
+def _grab_best_device(use_gpu=True):
+    if torch.cuda.device_count() > 0 and use_gpu:
+        device = "cuda"
+    else:
+        device = "cpu"
+    return device
+
+device = _grab_best_device()
+
+default_model_per_language = {
+    "marathi": "facebook/mms-tts-mar"
+}
+
+models_per_language = {
+    "marathi": ["ylacombe/mms-mar-finetuned-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=0),
+    "original_pipe": pipeline("text-to-speech", model=default_model_per_language["marathi"], device=0),
+    "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 = 1
+
+
+# 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=0)
+    
+    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=0)
+        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 = "marathi",
+                label = "language",
+                info = "Language that you want to test"
+            )
+            
+            model_id = gr.Dropdown(
+                    models_per_language["marathi"],
+                    value="ylacombe/mms-mar-finetuned-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).
+### Tamil
+* **Model**: [Tamil MMS TTS](https://huggingface.co/facebook/mms-tts-tam).
+* **Datasets**:
+    - [Tamil TTS dataset](https://huggingface.co/datasets/ylacombe/google-tamil).
+### Gujarati
+* **Model**: [Gujarati MMS TTS](https://huggingface.co/facebook/mms-tts-guj).
+* **Datasets**:
+    - [Gujarati TTS dataset](https://huggingface.co/datasets/ylacombe/google-gujarati).
+### Marathi
+* **Model**: [Marathi MMS TTS](https://huggingface.co/facebook/mms-tts-mar).
+* **Datasets**:
+    - [Marathi TTS dataset](https://huggingface.co/datasets/ylacombe/google-chilean-marathi).
+### 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())
+        ```
+        """
+        )
+
+
+    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
+                   )
+    
+    btn.click(generate_audio, [inp_text, model_id, language], outputs)
+    
+
+demo_blocks.queue().launch()