Added Runpod Module

Dockerfile

@@ -58,4 +58,5 @@ ENV PYTHONPATH=${HOME}/app \
     TQDM_POSITION=-1 \
     TQDM_MININTERVAL=1 \
     SYSTEM=spaces
-CMD ["python", "app.py"]
+# CMD ["python", "app.py"]
+CMD ["python", "server.py"]

requirements.txt

@@ -2,3 +2,4 @@ gradio==4.9.0
 omegaconf==2.3.0
 torch==2.1.0
 torchaudio==2.1.0
+runpod

server.py

@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import os
+import pathlib
+
+import gradio as gr
+import numpy as np
+import torch
+import torchaudio
+from fairseq2.assets import InProcAssetMetadataProvider, asset_store
+from huggingface_hub import snapshot_download
+from seamless_communication.inference import Translator
+
+from lang_list import (
+    ASR_TARGET_LANGUAGE_NAMES,
+    LANGUAGE_NAME_TO_CODE,
+    S2ST_TARGET_LANGUAGE_NAMES,
+    S2TT_TARGET_LANGUAGE_NAMES,
+    T2ST_TARGET_LANGUAGE_NAMES,
+    T2TT_TARGET_LANGUAGE_NAMES,
+    TEXT_SOURCE_LANGUAGE_NAMES,
+)
+
+# CHECKPOINTS_PATH = pathlib.Path(os.getenv("CHECKPOINTS_PATH", "/home/user/app/models"))
+# if not CHECKPOINTS_PATH.exists():
+#     snapshot_download(repo_id="facebook/seamless-m4t-v2-large", repo_type="model", local_dir=CHECKPOINTS_PATH)
+# asset_store.env_resolvers.clear()
+# asset_store.env_resolvers.append(lambda: "demo")
+# demo_metadata = [
+#     {
+#         "name": "seamlessM4T_v2_large@demo",
+#         "checkpoint": f"file://{CHECKPOINTS_PATH}/seamlessM4T_v2_large.pt",
+#         "char_tokenizer": f"file://{CHECKPOINTS_PATH}/spm_char_lang38_tc.model",
+#     },
+#     {
+#         "name": "vocoder_v2@demo",
+#         "checkpoint": f"file://{CHECKPOINTS_PATH}/vocoder_v2.pt",
+#     },
+# ]
+# asset_store.metadata_providers.append(InProcAssetMetadataProvider(demo_metadata))
+
+# if torch.cuda.is_available():
+#     device = torch.device("cuda:0")
+#     dtype = torch.float16
+# else:
+#     device = torch.device("cpu")
+#     dtype = torch.float32
+
+# translator = Translator(
+#     model_name_or_card="seamlessM4T_v2_large",
+#     vocoder_name_or_card="vocoder_v2",
+#     device=device,
+#     dtype=dtype,
+#     apply_mintox=True,
+# )
+
+# def run_t2tt(input_text: str, source_language: str, target_language: str) -> str:
+#     source_language_code = LANGUAGE_NAME_TO_CODE[source_language]
+#     target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
+#     out_texts, _ = translator.predict(
+#         input=input_text,
+#         task_str="T2TT",
+#         src_lang=source_language_code,
+#         tgt_lang=target_language_code,
+#     )
+#     return str(out_texts[0])
+
+import runpod
+
+def is_even(job):   
+
+    job_input = job["input"]
+    the_number = job_input["number"]
+
+    if not isinstance(the_number, int):
+        return {"error": "Silly human, you need to pass an integer."}
+
+    if the_number % 2 == 0:
+        return True
+    
+    return False
+
+    # output_text = run_t2tt(input_text, source_language, target_language)
+
+runpod.serverless.start({"handler": is_even})