test

app.py

@@ -19,22 +19,19 @@ pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 # pipe.unet = torch.compile(pipe.unet)
 #pipe.unet = torch.compile(pipe.unet)
-
-
-
 import base64
 
 with open("Iso_Logotipo_Ceibal.png", "rb") as image_file:
     encoded_image = base64.b64encode(image_file.read()).decode()
 
-CKPT = "facebook/nllb-200-distilled-600M"
+# CKPT = "facebook/nllb-200-distilled-600M"
 
-model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
-tokenizer = AutoTokenizer.from_pretrained(CKPT)
+# model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
+# tokenizer = AutoTokenizer.from_pretrained(CKPT)
 def generate_sound(text,steps,audio_length,negative_prompt): 
     print(text)
-    text=translate_text(text)
-    negative_prompt = translate_text(negative_prompt)
+    # text=translate_text(text)
+    # negative_prompt = translate_text(negative_prompt)
     print(text)
     waveforms = pipe(text, 
                      num_inference_steps=steps, 
@@ -43,17 +40,17 @@ def generate_sound(text,steps,audio_length,negative_prompt):
     rate =16000
     return rate, waveforms[0]
 
-def translate_text(text):
-    translation_pipeline = pipeline("translation",
-                                    model=model,
-                                    tokenizer=tokenizer,
-                                    src_lang="spa_Latn",
-                                    tgt_lang="eng_Latn",
-                                    max_length=400,
-                                    device=device)
+# def translate_text(text):
+#     translation_pipeline = pipeline("translation",
+#                                     model=model,
+#                                     tokenizer=tokenizer,
+#                                     src_lang="spa_Latn",
+#                                     tgt_lang="eng_Latn",
+#                                     max_length=400,
+#                                     device=device)
 
-    result = translation_pipeline(text)
-    return result[0]['translation_text']
+#     result = translation_pipeline(text)
+#     return result[0]['translation_text']
 # def translate_text(text):
 #     text = es_en_translator(text)[0].get("translation_text")
 #     return text