Update app.py

app.py

@@ -10,7 +10,6 @@ import torch
 import random
 from openai import OpenAI
 import subprocess
-import pandas as pd
 
 default_lang = "en"
 
@@ -98,46 +97,29 @@ LANGUAGE_CODES = {
     "Spanish": "spa",
     "French": "fra",
     "German": "deu",
-    "Chinese": "cmn",
-    "Italian": "ita"
+    "Italian": "ita",
+    "Chinese": "cmn"
 }
 
-MODEL_DIR = "/path/to/SeamlessExpressive/model"  # Update this path
-OUTPUT_DIR = "tmp/"
-
 def translate_speech(audio_file, target_language):
     """
-    Translate input speech (audio file) to the specified target language using facebook/seamless-expressive model.
+    Translate input speech (audio file) to the specified target language.
     """
     language_code = LANGUAGE_CODES[target_language]
-    
-    # Create a temporary TSV file
-    temp_tsv = tempfile.NamedTemporaryFile(mode='w+', delete=False, suffix='.tsv')
-    df = pd.DataFrame({'id': ['1'], 'audio': [audio_file]})
-    df.to_csv(temp_tsv.name, sep='\t', index=False)
-    temp_tsv.close()
-
-    output_file = os.path.join(OUTPUT_DIR, "translated_audio.wav")
+    output_file = "translated_audio.wav"
     
     command = [
-        "python", "src/seamless_communication/cli/expressivity/evaluate/pretssel_inference.py",
-        temp_tsv.name,
-        "--gated-model-dir", MODEL_DIR,
-        "--task", "s2st",
+        "expressivity_predict",
+        audio_file,
         "--tgt_lang", language_code,
-        "--audio_root_dir", "",
-        "--output_path", OUTPUT_DIR,
         "--model_name", "seamless_expressivity",
         "--vocoder_name", "vocoder_pretssel",
-        "--text_unk_blocking", "True",
-        "--duration_factor", "1.0"
+        "--gated-model-dir", "seamlessmodel",
+        "--output_path", output_file
     ]
     
     subprocess.run(command, check=True)
 
-    # Clean up the temporary TSV file
-    os.unlink(temp_tsv.name)
-
     if os.path.exists(output_file):
         print(f"File created successfully: {output_file}")
     else: