import gradio as gr import librosa import soundfile as sf import torch import warnings import os from transformers import Wav2Vec2ProcessorWithLM, Wav2Vec2CTCTokenizer, Wav2Vec2Model from engine import SpeechToTextEngine import wave import gradio as gr import librosa import soundfile as sf import warnings from nemo_asr import transcribe warnings.filterwarnings("ignore") from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-rw", savedir="pretrained_models/asr-wav2vec2-commonvoice-rw") #asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-rw/example.mp3") # define speech-to-text function def asr_transcript(audio): if audio == None: return "Please provide audio by uploading a file or by recording audio using microphone by pressing Record (And allow usage of microphone)", "Please provide audio by uploading a file or by recording audio using microphone by pressing Record (And allow usage of microphone)" text = "" data={} if audio: text_asr = asr_model.transcribe_file(audio.name) text_nemo_trasducer = transcribe(audio.name, "stt_rw_conformer_transducer_large") with open(audio.name,'rb') as f: audio_proper = f.read() stt_engine = SpeechToTextEngine() all_hot_words = [] if data: all_hot_words = stt_engine.add_hot_words(data) if not audio_proper: raise InvalidUsage('Audio not provided') # Running the transcription text_coqui = stt_engine.run(audio_proper) return text_asr.lower() , text_coqui , text_nemo_trasducer else: return "File not valid" gradio_ui = gr.Interface( fn=asr_transcript, title="Kinyarwanda Speech Recognition", description="Record an audio clip from browser using microphone, and let AI do the hard work of transcribing.", article = """ This demo showcases two pretrained STT models the first model from speechbrain(wave2vec+CTC models)(1,2gb) is 30 times larger compared to the coqui STT (deepspeech model)(45mb). """, inputs=[gr.inputs.Audio(source="microphone", type="file", optional=False, label="Record from microphone")], outputs=[gr.outputs.Textbox(label="Recognized speech from speechbrain model"), gr.outputs.Textbox(label="Recognized speech from coqui STT model"), gr.outputs.Textbox(label="Recognized speech from NVIDIA Conformer transduver large model")] # examples = [["sample_1.wav"],["sample_2.wav"]] ) gradio_ui.launch(enable_queue=True)