| import gradio as gr | |
| import soundfile as sf | |
| from scipy import signal | |
| import numpy as np | |
| import torch, torchaudio | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, pipeline | |
| MODEL_IS="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h" | |
| MODEL_FO="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h" | |
| torch.random.manual_seed(0) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model_is = Wav2Vec2ForCTC.from_pretrained(MODEL_IS).to(device) | |
| processor_is = Wav2Vec2Processor.from_pretrained(MODEL_IS) | |
| model_fo = Wav2Vec2ForCTC.from_pretrained(MODEL_FO).to(device) | |
| processor_fo = Wav2Vec2Processor.from_pretrained(MODEL_FO) | |
| pipe_is = pipeline(model=MODEL_IS) | |
| def readwav(a_f): | |
| wav, sr = sf.read(a_f, dtype=np.float32) | |
| if len(wav.shape) == 2: | |
| wav = wav.mean(1) | |
| if sr != 16000: | |
| wlen = int(wav.shape[0] / sr * 16000) | |
| wav = signal.resample(wav, wlen) | |
| return wav | |
| def recc(audio_file,model,processor): | |
| wav = readwav(audio_file) | |
| with torch.inference_mode(): | |
| input_values = processor(wav,sampling_rate=16000).input_values[0] | |
| input_values = torch.tensor(input_values, device=device).unsqueeze(0) | |
| logits = model(input_values).logits | |
| pred_ids = torch.argmax(logits, dim=-1) | |
| xcp = processor.batch_decode(pred_ids) | |
| return xcp[0] | |
| def recis(audio_file): | |
| single_output = recc(audio_file,model_is,processor_is) | |
| chunk_output = pipe_is(audio_file, chunk_length_s=6)['text'] | |
| return (single_output, chunk_output) | |
| def recfo(audio_file): | |
| return recc(audio_file,model_fo,processor_fo) | |
| bl = gr.Blocks() | |
| with bl: | |
| gr.Markdown( | |
| """ | |
| # W2V2 speech recognition | |
| Upload a file for recognition with | |
| https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h | |
| or https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h | |
| - For some reason, the huggingface 'Hosted inference API' on the model page does not currently work, but this does. | |
| - There is no language model (yet), so it can generate non-words. | |
| - Send errors/bugs to [email protected] | |
| """ | |
| ) | |
| with gr.Tabs(): | |
| with gr.TabItem("Icelandic"): | |
| with gr.Row(): | |
| audio_file = gr.Audio(type="filepath") | |
| with gr.Column(): | |
| whole_output = gr.Textbox(label="whole-file recognition") | |
| chunk_output = gr.Textbox("recognition with chunking") | |
| text_button = gr.Button("Recognise Icelandic") | |
| text_button.click(recis, inputs=audio_file, outputs=[whole_output,chunk_output]) | |
| with gr.TabItem("Faroese"): | |
| with gr.Row(): | |
| audio_file = gr.Audio(type="filepath") | |
| text_output = gr.Textbox() | |
| text_button = gr.Button("Recognise Faroese") | |
| text_button.click(recfo, inputs=audio_file, outputs=text_output) | |
| bl.launch() | |