Instructions to use BenguerineMohammed/nmt-seq2seq-translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenguerineMohammed/nmt-seq2seq-translator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BenguerineMohammed/nmt-seq2seq-translator") model = AutoModelForSeq2SeqLM.from_pretrained("BenguerineMohammed/nmt-seq2seq-translator") - Notebooks
- Google Colab
- Kaggle
| import speech_recognition as sr | |
| from typing import Optional | |
| from .languages import get_speech_code | |
| def speech_to_text( | |
| audio_file: Optional[str], | |
| language: str = "English", | |
| ) -> str: | |
| """Transcribe an audio file to text.""" | |
| if not audio_file: | |
| return "No audio file provided" | |
| try: | |
| recognizer = sr.Recognizer() | |
| lang_code = get_speech_code(language, fallback="en-US") | |
| with sr.AudioFile(audio_file) as source: | |
| audio_data = recognizer.record(source) | |
| return recognizer.recognize_google(audio_data, language=lang_code) | |
| except sr.UnknownValueError: | |
| return "couldn't understand audio" | |
| except sr.RequestError as exc: | |
| return f"Speech recognition service error: {exc}" | |
| except Exception as exc: | |
| return f"Error processing audio: {exc}" |