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import gradio as gr
import soundfile as sf
import numpy as np
import torch, torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, Audio
import matplotlib.pyplot as plt
MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h"
torch.random.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME).to(device)
processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME) # do i need this? can't remember
#def greet(name):
# return "Hello " + name + "!!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()
#api = gr.Interface.load("models/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h")
#iface.launch()
#ds = load_dataset("language-and-voice-lab/samromur_asr",split='train',streaming=True)
#ds = load_dataset("language-and-voice-lab/samromur_asr",split='test')
#ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
def show_ex(exnum):
#return(ds['audio_id'][exnum])
return(exnum)
def recc(a_f):
wav, sr = sf.read(a_f, dtype=np.float32)
if len(wav.shape) == 2:
wav = wav.mean(1)
if sr != 16000:
wlen = int(wav.shape[0] / sr * 16000)
wav = signal.resample(wav, wlen)
with torch.inference_mode():
wav = torch.from_numpy(wav).unsqueeze(0)
if torch.cuda.is_available():
wav = wav.cuda()
input_values = processor(wav).input_values
return input_values
bl = gr.Blocks()
with bl:
text_input = gr.Textbox()
text_output = gr.Textbox()
text_button = gr.Button("Run")
#text_button.click(show_ex, inputs=text_input, outputs=text_output)
audio_file = gr.Audio(type="filepath")
#ipt =
text_button.click(recc, inputs=audio_file, outputs=text_output)
bl.launch()
#https://mercury-docs.readthedocs.io/en/latest/deploy/hugging-face-spaces/
#https://huggingface.co/spaces/pplonski/deploy-mercury
#https://discuss.huggingface.co/t/deploy-interactive-jupyter-notebook-on-spaces-with-mercury/17000
#https://huggingface.co/docs/transformers/notebooks
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