Spaces:
Runtime error
Runtime error
| import torch | |
| import torch.nn.functional as F | |
| from transformers import WhisperForConditionalGeneration, WhisperProcessor | |
| from transformers.models.whisper.tokenization_whisper import LANGUAGES | |
| from transformers.pipelines.audio_utils import ffmpeg_read | |
| import gradio as gr | |
| device = "cuda" if torch.cuda.is_available() else "CPU" | |
| model_ckpt = "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_ckpt) | |
| tokenizer = AutoTokenizer.from_pretrained(model_ckpt) | |
| def detect_language(sentence): | |
| tokenized_sentence = tokenizer(sentence, return_tensors='pt') | |
| output = model(**tokenized_sentence) | |
| predictions = torch.nn.functional.softmax(output.logits, dim=-1) | |
| probability, pred_idx = torch.max(predictions, dim=-1) | |
| language = LANGUANGE_MAP[pred_idx.item()] | |
| return language, probability.item() | |
| def process_audio_file(file): | |
| with open(file, "rb") as f: | |
| inputs = f.read() | |
| audio = ffmpeg_read(inputs, sampling_rate) | |
| return audio | |
| def transcribe(Microphone, File_Upload): | |
| warn_output = "" | |
| if (Microphone 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 = Microphone | |
| elif (Microphone is None) and (File_Upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| elif Microphone is not None: | |
| file = Microphone | |
| else: | |
| file = File_Upload | |
| audio_data = process_audio_file(file) | |
| input_features = processor(audio_data, return_tensors="pt").input_features | |
| with torch.no_grad(): | |
| logits = model.forward(input_features.to(device), decoder_input_ids=decoder_input_ids).logits | |
| pred_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.decode(pred_ids[0]) | |
| detect_language(transcription.capitalize()) | |
| examples=['sample1.mp3', 'sample2.mp3', 'sample3.mp3'] | |
| outputs=gr.outputs.Label(label="Language detected:") | |
| article = """ | |
| Fine-tuned on xlm-roberta-base model.\n | |
| Supported languages:\n | |
| 'Arabic', 'Basque', 'Breton', 'Catalan', 'Chinese_China', 'Chinese_Hongkong', 'Chinese_Taiwan', 'Chuvash', 'Czech', | |
| 'Dhivehi', 'Dutch', 'English', 'Esperanto', 'Estonian', 'French', 'Frisian', 'Georgian', 'German', 'Greek', 'Hakha_Chin', | |
| 'Indonesian', 'Interlingua', 'Italian', 'Japanese', 'Kabyle', 'Kinyarwanda', 'Kyrgyz', 'Latvian', 'Maltese', | |
| 'Mangolian', 'Persian', 'Polish', 'Portuguese', 'Romanian', 'Romansh_Sursilvan', 'Russian', 'Sakha', 'Slovenian', | |
| 'Spanish', 'Swedish', 'Tamil', 'Tatar', 'Turkish', 'Ukranian', 'Welsh' | |
| """ | |
| gr.Interface( | |
| fn=detect_language, | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type='filepath', optional=True), | |
| gr.inputs.Audio(source="upload", type='filepath', optional=True), | |
| ], | |
| outputs=outputs=[ | |
| gr.outputs.Textbox(label="Language"), | |
| gr.Number(label="Probability"), | |
| ], | |
| verbose=True, | |
| examples = examples, | |
| title="Language Identification from Audio", | |
| description="Detect the Language from Audio.", | |
| article=article, | |
| theme="huggingface" | |
| ).launch() | |