import gradio as gr

LAST_UPDATED = "Nov 25th 2024"

####################################
# Datos estáticos del leaderboard
####################################
leaderboard_data = [
    {'name': 'StyleTTS 2', 'STOI': 0.998, 'PESQ': 3.921, 'WER': 0.162, 'UTMOS': 3.47},
    {'name': 'Matxa-TTS', 'STOI': 0.996, 'PESQ': 3.539, 'WER': 0.179, 'UTMOS': 3.50},
    {'name': 'Matxa-TTS-multiaccent', 'STOI': 0.996, 'PESQ': 3.415, 'WER': 0.242, 'UTMOS': 2.98},
    {'name': 'StableTTS', 'STOI': 0.997, 'PESQ': 3.643, 'WER': 0.164, 'UTMOS': 2.62},
]


# Texto para la pestaña de métricas
METRICS_TAB_TEXT = """
## Metrics

Models in the leaderboard are evaluated using several key metrics: 
* **UTMOS** (UTokyo-SaruLab Mean Opinion Score),
* **WER** (Word Error Rate),
* **STOI** (Short-Time Objective Intelligibility),
* **PESQ** (Perceptual Evaluation of Speech Quality).

These metrics help evaluate both the accuracy and quality of the model.

### UTMOS (UTokyo-SaruLab Mean Opinion Score)[[Paper](https://arxiv.org/abs/2204.02152)]
UTMOS is a MOS prediction system. **A higher UTMOS indicates better quality** of the generated voice.


### WER (Word Error Rate)
WER is a common metric for evaluating speech recognition systems. It measures the percentage of words in the generated transcript that differ from the reference (correct) transcript. **A lower WER value indicates higher accuracy**.

Example:
| Reference   | the  | cat | sat     | on  | the  | mat |
|-------------|------|-----|---------|-----|------|-----|
| Prediction  | the  | cat | **sit** | on  | the  |     |
| Label       | ✅   | ✅  | S       | ✅  | ✅   | D   |

The WER calculation is done as follows:


```
WER = (S + I + D) / N = (1 + 0 + 1) / 6 = 0.333
```

### STOI (Short-Time Objective Intelligibility)[[Paper](https://ieeexplore.ieee.org/abstract/document/5495701?casa_token=PLtqLc8KNAgAAAAA:FOLuZ4dgMYsnGb1dQHgqVOouQzRJ3vA5yqj-sbwf8gs9Q-AIDCLkMZzAgzRrAogwwxULK9zsYeE)]
STOI measures the intelligibility of the synthesized speech signal compared to the original signal. **A higher STOI indicates better intelligibility**.

### PESQ (Perceptual Evaluation of Speech Quality)[[Paper](https://ieeexplore.ieee.org/abstract/document/941023?casa_token=jdtHy84_KhQAAAAA:qHN3WbT6cNdufj6OOn_fn0Je0RedMv-WJCmhQ_3CWy4nMTuDvFMF3KstAmKqLx5suQwdPgGByoY)]
PESQ is a perceptual metric that evaluates the quality of speech in a similar manner to how a human listener would. **A higher PESQ indicates better voice quality**.


## Benchmark Datasets
Model performance is evaluated using [our test datasets](https://huggingface.co/spaces/rjzevallos/test_app/blob/main/bsc.txt). These datasets cover a variety of domains and acoustic conditions, ensuring a robust evaluation.
"""



####################################
# Functions (static version)
####################################

def get_leaderboard():
    """
    Retorna el leaderboard en orden descendente por PESQ y luego por UTMOS.
    """
    # Ordenar primero por PESQ (calidad del habla) y luego por UTMOS (calidad percibida)
    sorted_leaderboard = sorted(leaderboard_data, key=lambda x: (x['UTMOS']), reverse=True)
    
    # Asignar el rank basado en el orden por PESQ
    for rank, model in enumerate(sorted_leaderboard):
        model['rank'] = rank + 1  # rank es la posición en la lista (1-indexed)
    
    return [[model['rank'], model['name'], model['UTMOS'], model['WER'], model['STOI'], model['PESQ']] for model in sorted_leaderboard]

####################################
# Interfaz con Gradio
####################################

theme = gr.themes.Base(
    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)

with gr.Blocks(theme=theme) as demo:
    gr.Markdown("# 🏆 Leaderboard\nVote to help the community determine the best Catalan TTS models.\n")
    

    
with gr.Blocks(theme=theme) as demo:
    gr.Markdown("# 🏆 Leaderboard\nVote to help the community determine the best Catalan TTS models.\n")
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0):
            leaderboard_table = gr.DataFrame(
                headers=["Rank", "Model", "UTMOS", "WER", "STOI", "PESQ"], 
                datatype=["str", "str", "str", "str", "str", "str"], 
                value=get_leaderboard()  # Carga los datos iniciales de la tabla
            )

        with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
            gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")

        
    gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text")



# Lanzar la aplicación
demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False)