Create app.py

app.py

@@ -0,0 +1,54 @@
+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
+
+access_token_read = os.environ.get('HF_TOKEN', None)
+login(token = access_token_read)
+
+os.mkdir(os.path.join("data", "hfcache"))
+os.mkdir(os.path.join("data", "audio"))
+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")
+
+    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(os.path.join("data", "audio_texts", f"{uid}.txt"), "w", encoding="utf-8") as f:
+        f.write(text)
+
+    write(os.path.join("data", "audio", f"{uid}.wav"), sr, y)
+    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)