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| from transformers import AutoProcessor, AutoModelForCTC | |
| from transformers import pipeline | |
| import soundfile as sf | |
| import gradio as gr | |
| import librosa | |
| import torch | |
| import sox | |
| import os | |
| from image_generator import generate_image | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| api_token = os.getenv("API_TOKEN") | |
| asr_processor = AutoProcessor.from_pretrained("imvladikon/wav2vec2-xls-r-300m-hebrew") | |
| asr_model = AutoModelForCTC.from_pretrained("imvladikon/wav2vec2-xls-r-300m-hebrew") | |
| he_en_translator = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-he-en") | |
| def process_audio_file(file): | |
| data, sr = librosa.load(file) | |
| if sr != 16000: | |
| data = librosa.resample(data, sr, 16000) | |
| input_values = processor(data, sampling_rate=16_000, return_tensors="pt").input_values #.to(device) | |
| return input_values | |
| def transcribe(file_mic, file_upload): | |
| warn_output = "" | |
| if (file_mic is not None) and (file_upload is not None): | |
| warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| file = file_mic | |
| elif (file_mic is None) and (file_upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| elif file_mic is not None: | |
| file = file_mic | |
| else: | |
| file = file_upload | |
| input_values = process_audio_file(file) | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| return warn_output + transcription | |
| def convert(inputfile, outfile): | |
| sox_tfm = sox.Transformer() | |
| sox_tfm.set_output_format( | |
| file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16 | |
| ) | |
| sox_tfm.build(inputfile, outfile) | |
| def generate_image(text): | |
| pass | |
| def parse_transcription(wav_file): | |
| filename = wav_file.name.split('.')[0] | |
| convert(wav_file.name, filename + "16k.wav") | |
| speech, _ = sf.read(filename + "16k.wav") | |
| print(speech.shape) | |
| input_values = asr_processor(speech, sampling_rate=16_000, return_tensors="pt").input_values | |
| logits = asr_model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = asr_processor.decode(predicted_ids[0], skip_special_tokens=True) | |
| translated = he_en_translator(transcription)[0]['translation_text'] | |
| image = generate_image(translated) | |
| return image | |
| output = gr.outputs.Image(label='') | |
| input_mic = gr.inputs.Audio(source="microphone", type="file", optional=True) | |
| input_upload = gr.inputs.Audio(source="upload", type="file", optional=True) | |
| gr.Interface(parse_transcription, inputs=[input_mic], outputs=output, | |
| analytics_enabled=False, | |
| show_tips=False, | |
| theme='huggingface', | |
| layout='horizontal', | |
| title="Draw Me A Sheep in Hebrew", | |
| enable_queue=True).launch(inline=False) |