app.py

import sys
import time

from importlib.metadata import version

import torch
import torchaudio
import torchaudio.transforms as T

import gradio as gr
import numpy as np

from transformers import HubertForCTC, Wav2Vec2Processor

# Config
model_name = "Yehor/mHuBERT-147-uk"

min_duration = 0.5
max_duration = 60

concurrency_limit = 5
use_torch_compile = False

# Torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Load the model
asr_model = HubertForCTC.from_pretrained(model_name, torch_dtype=torch_dtype, device_map=device)
processor = Wav2Vec2Processor.from_pretrained(model_name)

if use_torch_compile:
 asr_model = torch.compile(asr_model)

# Elements
examples = [
 "example_1.wav",
 "example_2.wav",
 "example_3.wav",
 "example_4.wav",
 "example_5.wav",
 "example_6.wav",
]

examples_table = """
| File | Text |
| ------------- | ------------- |
| `example_1.wav` | тема про яку не люблять говорити офіційні джерела у генштабі і міноборони це хімічна зброя окупанти вже тривалий час використовують хімічну зброю заборонену |
| `example_2.wav` | всіма конвенціями якщо спочатку це були гранати з дронів то тепер фіксують випадки застосування |
| `example_3.wav` | хімічних снарядів причому склад отруйної речовони різний а отже й наслідки для наших військових теж різні |
| `example_4.wav` | використовує на фронті все що має і хімічна зброя не вийняток тож з чим маємо справу розбиралася марія моганисян |
| `example_5.wav` | двох тисяч випадків застосування росіянами боєприпасів споряджених небезпечними хімічними речовинами |
| `example_6.wav` | на всі писані норми марія моганисян олександр моторний спецкор марафон єдині новини |
""".strip()

# https://www.tablesgenerator.com/markdown_tables
authors_table = """
## Authors

Follow them in social networks and **contact** if you need any help or have any questions:

| <img src="https://avatars.githubusercontent.com/u/7875085?v=4" width="100"> **Yehor Smoliakov** |
|-------------------------------------------------------------------------------------------------|
| https://t.me/smlkw in Telegram |
| https://x.com/yehor_smoliakov at X |
| https://github.com/egorsmkv at GitHub |
| https://huggingface.co/Yehor at Hugging Face |
| or use [email protected] |
""".strip()

description_head = f"""
# Speech-to-Text for Ukrainian using HuBERT

## Overview

This space uses https://huggingface.co/Yehor/mHuBERT-147-uk model to recognize audio files.

> Due to resource limitations, audio duration **must not** exceed **{max_duration}** seconds.
""".strip()

description_foot = f"""
## Community

- **Discord**: https://discord.gg/yVAjkBgmt4
- Speech Recognition: https://t.me/speech_recognition_uk
- Speech Synthesis: https://t.me/speech_synthesis_uk

## More

Check out other ASR models: https://github.com/egorsmkv/speech-recognition-uk

{authors_table}
""".strip()

transcription_value = """
Recognized text will appear here.

Choose **an example file** below the Recognize button, upload **your audio file**, or use **the microphone** to record own voice.
""".strip()

tech_env = f"""
#### Environment

- Python: {sys.version}
- Torch device: {device}
- Torch dtype: {torch_dtype}
- Use torch.compile: {use_torch_compile}
""".strip()

tech_libraries = f"""
#### Libraries

- torch: {version('torch')}
- torchaudio: {version('torchaudio')}
- transformers: {version('transformers')}
- accelerate: {version('accelerate')}
- gradio: {version('gradio')}
""".strip()


def inference(audio_path, progress=gr.Progress()):
 if not audio_path:
 raise gr.Error("Please upload an audio file.")

 gr.Info("Starting recognition", duration=2)

 progress(0, desc="Recognizing")

 meta = torchaudio.info(audio_path)
 duration = meta.num_frames / meta.sample_rate

 if duration < min_duration:
 raise gr.Error(
 f"The duration of the file is less than {min_duration} seconds, it is {round(duration, 2)} seconds."
 )
 if duration > max_duration:
 raise gr.Error(f"The duration of the file exceeds {max_duration} seconds.")

 paths = [
 audio_path,
 ]

 results = []

 for path in progress.tqdm(paths, desc="Recognizing...", unit="file"):
 t0 = time.time()

 meta = torchaudio.info(audio_path)
 audio_duration = meta.num_frames / meta.sample_rate

 audio_input, sr = torchaudio.load(path)

 if meta.num_channels > 1:
 audio_input = torch.mean(audio_input, dim=0, keepdim=True)

 if meta.sample_rate != 16_000:
 resampler = T.Resample(sr, 16_000, dtype=audio_input.dtype)
 audio_input = resampler(audio_input)

 audio_input = audio_input.squeeze(0).numpy()

 inputs = processor([audio_input], sampling_rate=16_000, padding=True).input_values
 features = torch.tensor(np.array(inputs), dtype=torch_dtype).to(device)

 with torch.inference_mode():
 logits = asr_model(features).logits

 predicted_ids = torch.argmax(logits, dim=-1)
 predictions = processor.batch_decode(predicted_ids)

 if not predictions:
 predictions = "-"

 elapsed_time = round(time.time() - t0, 2)
 rtf = round(elapsed_time / audio_duration, 4)
 audio_duration = round(audio_duration, 2)

 results.append(
 {
 "path": path.split("/")[-1],
 "transcription": "\n".join(predictions),
 "audio_duration": audio_duration,
 "rtf": rtf,
 }
 )

 gr.Info("Finished!", duration=2)

 result_texts = []

 for result in results:
 result_texts.append(f'**{result["path"]}**')
 result_texts.append("\n\n")
 result_texts.append(f'> {result["transcription"]}')
 result_texts.append("\n\n")
 result_texts.append(f'**Audio duration**: {result["audio_duration"]}')
 result_texts.append("\n")
 result_texts.append(f'**Real-Time Factor**: {result["rtf"]}')

 return "\n".join(result_texts)


demo = gr.Blocks(
 title="Speech-to-Text for Ukrainian",
 analytics_enabled=False,
 theme=gr.themes.Base(),
)

with demo:
 gr.Markdown(description_head)

 gr.Markdown("## Usage")

 with gr.Row():
 audio_file = gr.Audio(label="Audio file", type="filepath")
 transcription = gr.Markdown(
 label="Transcription",
 value=transcription_value,
 )

 gr.Button("Recognize").click(
 inference,
 concurrency_limit=concurrency_limit,
 inputs=audio_file,
 outputs=transcription,
 )

 with gr.Row():
 gr.Examples(label="Choose an example", inputs=audio_file, examples=examples)

 gr.Markdown(examples_table)

 gr.Markdown(description_foot)

 gr.Markdown("### Gradio app uses the following technologies:")
 gr.Markdown(tech_env)
 gr.Markdown(tech_libraries)

if __name__ == "__main__":
 demo.queue()
 demo.launch()