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3367919 24ca5e6 3625f09 24ca5e6 3367919 3625f09 3367919 3625f09 9129e28 169a9ce 0fc0029 3367919 83c8a3c 3625f09 83c8a3c 3367919 55de06b 3367919 3625f09 3367919 3625f09 3367919 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | import gradio as gr
from transformers import pipeline
import numpy as np
import os
from huggingface_hub import login
from scipy.io.wavfile import write
import uuid
import torch
import boto3
print("cuda", torch.cuda.is_available())
access_token_read = os.environ.get('HF_TOKEN', None)
access_key = os.environ.get('access_key', None)
secret_access_key = os.environ.get('secret_access_key', None)
login(token = access_token_read)
session = boto3.Session(
aws_access_key_id=access_key,
aws_secret_access_key=secret_access_key,
)
s3 = session.resource('s3')
BUCKET = "audio-text-938"
print("cur path", os.listdir(os.path.join("..", "..", "..")))
if not os.path.isdir(os.path.join("..", "..", "..", "data", "hfcache")):
os.mkdir(os.path.join("..", "..", "..", "data", "hfcache"))
if not os.path.isdir(os.path.join("..", "..", "..", "data", "audio")):
os.mkdir(os.path.join("..", "..", "..", "data", "audio"))
if not os.path.isdir(os.path.join("..", "..", "..", "data", "audio_texts")):
os.mkdir(os.path.join("..", "..", "..", "data", "audio_texts"))
os.environ["HF_HOME"] = os.path.join("..", "..", "..", "data", "hfcache")
transcriber = pipeline("automatic-speech-recognition", model='Simranjit/whisper-medical-french', device="cuda")
def transcribe(audio):
sr, y = audio
y = y.astype(np.float32)
y /= np.max(np.abs(y))
text = transcriber({"sampling_rate": sr, "raw": y})["text"]
text = text.replace("nouvelle ligne", "\n")
text = text.replace("à la ligne", "\n")
text = text.replace(" virgule", ",")
text = text.replace(" virgule", ",")
text = text.replace(" deux points", ":")
text = text.replace(" deux points", ":")
text = text.replace(" point", ".")
text = text.replace(" point", ".")
text = text.replace(" nouveau paragraphe ", "\n\n")
text = text.replace(" paragraphe ", "\n\n")
return text
def save_fn(audio, text):
sr, y = audio
y = y.astype(np.float32)
y /= np.max(np.abs(y))
uid = str(uuid.uuid4())
with open(f"{uid}.txt", "w", encoding="utf-8") as f:
f.write(text)
s3.Bucket(BUCKET).upload_file(f"{uid}.txt", f"texts/{uid}.txt") #local path, bucket path
write(f"{uid}.wav", sr, y)
s3.Bucket(BUCKET).upload_file(f"{uid}.wav", f"audios/{uid}.wav") #local path, bucket path
return [None, ""]
with gr.Blocks() as demo:
audio = gr.Audio()
text = gr.TextArea(show_copy_button=True)
btn = gr.Button("run")
btn.click(fn=transcribe, inputs=audio, outputs=text)
save = gr.Button("save")
save.click(fn=save_fn, inputs=[audio, text], outputs=[audio, text])
demo.launch(share=True) |