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import gradio as gr |
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import librosa |
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import soundfile as sf |
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import torch |
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import warnings |
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import os |
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from transformers import Wav2Vec2ProcessorWithLM, Wav2Vec2CTCTokenizer |
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warnings.filterwarnings("ignore") |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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from fastapi import FastAPI, HTTPException, File |
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from transformers import pipeline |
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pipe_95m = pipeline(model="Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned",chunk_length_s=20, stride_length_s=(3, 3)) |
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pipe_300m = pipeline(model="Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish",chunk_length_s=20, stride_length_s=(3, 3)) |
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pipe_1b = pipeline(model="Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2",chunk_length_s=20, stride_length_s=(3, 3)) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model_checkpoint = 'Finnish-NLP/t5-small-nl24-casing-punctuation-correction' |
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, from_flax=False, torch_dtype=torch.float32, use_auth_token=os.environ.get('hf_token')).to(device) |
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def asr_transcript(audio, audio_microphone, model_params): |
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audio = audio_microphone if audio_microphone else audio |
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if audio == None and audio_microphone == None: |
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return "Please provide audio (wav or mp3) 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)" |
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text = "" |
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if audio: |
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if model_params == "1 billion": |
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text = pipe_1b(audio.name) |
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elif model_params == "300 million": |
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text = pipe_300m(audio.name) |
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elif model_params == "95 million": |
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text = pipe_95m(audio.name) |
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input_ids = tokenizer(text['text'], return_tensors="pt").input_ids.to(device) |
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outputs = model.generate(input_ids, max_length=128) |
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case_corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return text['text'], case_corrected_text |
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else: |
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return "File not valid" |
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gradio_ui = gr.Interface( |
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fn=asr_transcript, |
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title="Finnish Automatic Speech Recognition", |
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description="Upload an audio clip or record from browser using microphone, and let AI do the hard work of transcribing.", |
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article = """ |
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This demo includes 2 kinds of models that are run together. First selected ASR model does speech recognition which produces lowercase text without punctuation. |
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After that we run a sequence-to-sequence model which tries to correct casing and punctuation which produces the final output. |
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You can select one of two speech recognition models listed below |
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1. 1 billion, best accuracy but slowest by big margin. Based on multilingual wav2vec2-xlsr model by Meta. More info here https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 |
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3. 300 million, at bar in accuracy as 1. but a lot faster. Based on Uralic wav2vec2 model by Meta. More info here https://huggingface.co/Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish |
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3. 95 million, almost as accurate as 1. but really much faster. Based on Finnish wav2vec2 model by Meta. More info here https://huggingface.co/Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned |
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More info about the casing+punctuation correction model can be found here https://huggingface.co/Finnish-NLP/t5-small-nl24-casing-punctuation-correction |
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""", |
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inputs=[gr.inputs.Audio(label="Upload Audio File", type="file", optional=True), gr.inputs.Audio(source="microphone", type="file", optional=True, label="Record from microphone"), gr.inputs.Dropdown(choices=["95 million","300 million", "1 billion"], type="value", default="300 million", label="Select speech recognition model parameter amount", optional=False)], |
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outputs=[gr.outputs.Textbox(label="Recognized speech"),gr.outputs.Textbox(label="Recognized speech with case correction and punctuation")] |
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) |
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gradio_ui.launch() |
