Update app.py

app.py

@@ -1,15 +1,53 @@
-# https://www.gradio.app/guides/quickstart
-# https://www.gradio.app/guides/key-features
+# https://github.com/openai/whisper/discussions/categories/show-and-tell
+import wavio as wv
+import datetime
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import torch
+
+from dotenv import load_dotenv
+import os 
+import whisper
+import ffmpeg 
+
 import gradio as gr
-import time
-def greet(name):
-    message_to_user="start here"
-#    gr.Info("message1")
-#    time.sleep(4)
-#    gr.Info("message2")
-#    gr.Warning("warning")
-    return "Hello " + name + "!"
-
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-
-demo.launch()
+from transformers import pipeline
+p = pipeline("automatic-speech-recognition")
+
+basedir = os.path.abspath(os.path.dirname(__file__))
+load_dotenv(os.path.join(basedir, '.env'))
+OPENAI_API_KEY=os.getenv("OPENAI_API_KEY")
+
+whisper_model = whisper.load_model("base")
+
+# this model was loaded from https://hf.co/models
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+device = 0 if torch.cuda.is_available() else -1
+LANGS = ["ace_Arab", "eng_Latn", "fra_Latn", "spa_Latn", "yue_Hant","zho_Hans","zho_Hant"]
+LANGS_source = ["eng_Latn"]
+
+# Yue Chinese - yue_Hant, Chinese (Simplified)-Zho_Hans, Chinese(Traditional)-zho_Hant
+# https://github.com/facebookresearch/flores/tree/main/flores200#languages-in-flores-200
+
+def translate(text, src_lang, tgt_lang):
+    """
+    Translate the text from source lang to target lang
+    """
+    translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang, max_length=400, device=device)
+    result = translation_pipeline(text)
+    return result[0]['translation_text']
+
+def transcribe(audio):
+#    text_audio = p(audio)["text"]
+    text_for_audio = whisper_model.transcribe(audio)
+    text_from_whisper = text_for_audio["text"]
+    text=translate(text_from_whisper,"eng_Latn","zho_Hans")
+    return text
+
+gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text").launch()
+