| import soundfile as sf |
| import torch |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
| from pyctcdecode import build_ctcdecoder |
| import gradio as gr |
| import librosa |
| import os |
| from multiprocessing import Pool |
|
|
|
|
| class KenLM: |
| def __init__(self, tokenizer, model_name, num_workers=8, beam_width=128): |
| self.num_workers = num_workers |
| self.beam_width = beam_width |
| vocab_dict = tokenizer.get_vocab() |
| self.vocabulary = [x[0] for x in sorted(vocab_dict.items(), key=lambda x: x[1], reverse=False)] |
| |
| self.vocabulary = self.vocabulary[:-2] |
| self.decoder = build_ctcdecoder(self.vocabulary, model_name) |
|
|
| @staticmethod |
| def lm_postprocess(text): |
| return ' '.join([x if len(x) > 1 else "" for x in text.split()]).strip() |
|
|
| def decode(self, logits): |
| probs = logits.cpu().numpy() |
| |
| with Pool(self.num_workers) as pool: |
| text = self.decoder.decode_batch(pool, probs) |
| text = [KenLM.lm_postprocess(x) for x in text] |
| return text |
|
|
|
|
| def convert(inputfile, outfile): |
| target_sr = 16000 |
| data, sample_rate = librosa.load(inputfile) |
| data = librosa.resample(data, orig_sr=sample_rate, target_sr=target_sr) |
| sf.write(outfile, data, target_sr) |
|
|
|
|
| api_token = os.getenv("API_TOKEN") |
| model_name = "indonesian-nlp/wav2vec2-luganda" |
| processor = Wav2Vec2Processor.from_pretrained(model_name, use_auth_token=api_token) |
| model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=api_token) |
| kenlm = KenLM(processor.tokenizer, "5gram.bin") |
|
|
|
|
| def parse_transcription(wav_file): |
| filename = wav_file.name.split('.')[0] |
| convert(wav_file.name, filename + "16k.wav") |
| speech, _ = sf.read(filename + "16k.wav") |
| input_values = processor(speech, sampling_rate=16_000, return_tensors="pt").input_values |
| with torch.no_grad(): |
| logits = model(input_values).logits |
| transcription = kenlm.decode(logits)[0] |
| return transcription |
|
|
|
|
| output = gr.outputs.Textbox(label="The transcript") |
|
|
| input_ = gr.inputs.Audio(source="microphone", type="file") |
|
|
| gr.Interface(parse_transcription, inputs=input_, outputs=[output], |
| analytics_enabled=False, |
| title="Automatic Speech Recognition for Luganda", |
| description="Speech Recognition Live Demo for Luganda", |
| article="This demo was built for the " |
| "<a href='https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition' target='_blank'>Mozilla Luganda Automatic Speech Recognition Competition</a>. " |
| "It uses the <a href='https://huggingface.co/indonesian-nlp/wav2vec2-luganda' target='_blank'>indonesian-nlp/wav2vec2-luganda</a> model " |
| "which was fine-tuned on Luganda Common Voice speech datasets.", |
| enable_queue=True).launch(inline=False, server_name="0.0.0.0", show_tips=False, enable_queue=True) |
