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import gradio as gr
import soundfile as sf
from scipy import signal
import numpy as np
import torch, torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
MODEL_IS="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h"
MODEL_FO="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h"
torch.random.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_is = Wav2Vec2ForCTC.from_pretrained(MODEL_IS).to(device)
processor_is = Wav2Vec2Processor.from_pretrained(MODEL_IS)
model_fo = Wav2Vec2ForCTC.from_pretrained(MODEL_FO).to(device)
processor_fo = Wav2Vec2Processor.from_pretrained(MODEL_FO)
def readwav(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)
return wav
def recc(audio_file,model,processor):
wav = readwav(audio_file)
with torch.inference_mode():
input_values = processor(wav,sampling_rate=16000).input_values[0]
input_values = torch.tensor(input_values, device=device).unsqueeze(0)
logits = model(input_values).logits
pred_ids = torch.argmax(logits, dim=-1)
xcp = processor.batch_decode(pred_ids)
return xcp[0]
def recis(audio_file):
return recc(audio_file,model_is,processor_is)
def recfo(audio_file):
return recc(audio_file,model_fo,processor_fo)
bl = gr.Blocks()
with bl:
gr.Markdown(
"""
# W2V2 speech recognition
Upload a file for recognition with
https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h
or https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h
- For some reason, the huggingface 'Hosted inference API' on the model page does not currently work, but this does.
- There is no language model (yet), so it can generate non-words.
- Send errors/bugs to [email protected]
"""
)
with gr.Tabs():
with gr.TabItem("Icelandic"):
with gr.Row():
audio_file = gr.Audio(type="filepath")
text_output = gr.Textbox()
text_button = gr.Button("Recognise Icelandic")
text_button.click(recis, inputs=audio_file, outputs=text_output)
with gr.TabItem("Faroese"):
with gr.Row():
audio_file = gr.Audio(type="filepath")
text_output = gr.Textbox()
text_button = gr.Button("Recognise Faroese")
text_button.click(recfo, inputs=audio_file, outputs=text_output)
bl.launch()
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