app.py

import torchaudio
from speechbrain.inference.ASR import EncoderASR
from speechbrain.dataio.encoder import CTCTextEncoder
from pyctcdecode import build_ctcdecoder
import torch
import speechbrain as sb




import logging
from huggingface_hub import hf_hub_download  # Add this import

# Set up logging
logging.basicConfig(level=logging.INFO)

# Download the checkpoint from Hugging Face Hub
checkpoint_path = hf_hub_download(
    repo_id="brdhaker3/TunASR",
    filename="model/1234/save/CKPT+2024-05-27+00-52-30+00/wav2vec2.ckpt",  # Path to your checkpoint

    local_dir="./",  # Save it to a local directory
)
logging.info(f"Checkpoint downloaded to: {checkpoint_path}")





# Load the ASR model
asr_model = EncoderASR.from_hparams(
    source="brdhaker3/TunASR",
    savedir = "./model"
)

# Loading Custom Tokenizer
encoder = CTCTextEncoder()
encoder.load_or_create(
    path=asr_model.hparams.encoder_file,
    from_didatasets=[[]],
    output_key="char_list",
    special_labels={"blank_label": 0, "unk_label": 1},
    sequence_input=True,
)
asr_model.tokenizer = encoder

# Prepare labels for the CTC decoder
vocab = asr_model.tokenizer.ind2lab
labels = [vocab[i] for i in range(len(vocab))]  # Extract labels from the tokenizer
labels = [""] + labels[1:-1] + ["1"]  # Adjust labels to match CTC format

# Initialize the CTC decoder with a language model
decoder = build_ctcdecoder(
    labels,
    kenlm_model_path=asr_model.hparams.ngram_lm_path,  # Path to your LM
    alpha=0.5,  # LM weight
    beta=1.0,   # Word insertion penalty
)
class ASR(sb.core.Brain):
    def treat_wav(self, sig):
        """Process a waveform and return the transcribed text."""
        feats = self.modules.wav2vec2(sig.to("cpu"), torch.tensor([1]).to("cpu"))
        feats = self.modules.enc(feats)
        logits = self.modules.ctc_lin(feats)
        p_ctc = self.hparams.log_softmax(logits)
        predicted_words = []
        for logs in p_ctc:
            text = decoder.decode(logs.detach().cpu().numpy())
            predicted_words.append(text.split(" "))
        return " ".join(predicted_words[0])
# Initialize the ASR model
asr_brain = ASR(
    modules=asr_model.hparams.modules,
    hparams=vars(asr_model.hparams),
    run_opts={"device": "cpu"},
    checkpointer=asr_model.hparams.checkpointer,
)
asr_brain.tokenizer = encoder
asr_brain.checkpointer.recover_if_possible()
asr_brain.modules.eval()
def treat_wav_file(file_mic, file_upload, asr=asr_brain, device="cpu"):
    if file_mic is not None:
        wav = file_mic
    elif file_upload is not None:
        wav = file_upload
    else:
        return "ERROR: You have to either use the microphone or upload an audio file"

    # Read and preprocess the audio file
    info = torchaudio.info(wav)
    sr = info.sample_rate
    sig = sb.dataio.dataio.read_audio(wav)
    if len(sig.shape) > 1:
        sig = torch.mean(sig, dim=1)
    sig = torch.unsqueeze(sig, 0)
    tensor_wav = sig.to(device)
    resampled = torchaudio.functional.resample(tensor_wav, sr, 16000)

    # Transcribe the audio
    sentence = asr.treat_wav(resampled)
    return sentence

# Test the function
# print(treat_wav_file("./audio.wav", "./audio.wav"))


#Gradio interface
import gradio as gr

title = "Tunisian Speech Recognition"

description = ''' 
This is a Tunisian ASR based on the **WavLM Model**, fine-tuned on a dataset of **2.5 hours**, resulting in a **W.E.R of 24%** and a **C.E.R of 9%**.  
\n
Interested? Try it out!
'''

disclaimer = '''
> ⚠️ **Disclaimer:**  
> This is a **demo model**, The transcription accuracy isn't accurate due to Hugging Face model storage constraints.  
> For better performance,you can run the full model locally.  
> Please check out the repository and follow the instructions: [Full Model Repo Link](https://huggingface.co/brdhaker3/TunASR)  
'''

with gr.Blocks() as demo:
    gr.Markdown(f"# {title}")
    gr.Markdown(description)
    gr.Markdown(disclaimer)

    interface = gr.Interface(
        fn=treat_wav_file,
        inputs=[
            gr.Audio(sources="microphone", type='filepath', label="Record"),
            gr.Audio(sources="upload", type='filepath', label="Upload File")
        ],
        outputs="text",
        title="",
        description=""
    )
    

demo.launch()