traduction

app.py

@@ -6,19 +6,27 @@ import os
 from huggingface_hub import InferenceClient, login
 import logging
 
-app = Flask(__name__)
+# Configurer les logs de debug
+logging.basicConfig(level=logging.DEBUG)
 
- 
+app = Flask(__name__)
 
-processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large" )
-model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
+# Définir et créer le dossier de cache pour Hugging Face
+HUGGINGFACE_CACHE_DIR = "./huggingface_cache"
+os.makedirs(HUGGINGFACE_CACHE_DIR, exist_ok=True)
+logging.debug("Dossier de cache Hugging Face : %s", HUGGINGFACE_CACHE_DIR)
 
-# Charger le processor et le modèle avec affichage des logs
+# Charger le processor et le modèle en utilisant le cache
 logging.debug("Début du chargement du processor et du modèle...")
-processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
-logging.debug("Processor chargé avec succès : %s", processor)
-model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
-logging.debug("Modèle chargé avec succès : %s", model)
+processor = AutoProcessor.from_pretrained(
+    "facebook/seamless-m4t-v2-large", cache_dir=HUGGINGFACE_CACHE_DIR
+)
+logging.debug("Processor chargé avec succès et mis en cache : %s", processor)
+model = SeamlessM4Tv2Model.from_pretrained(
+    "facebook/seamless-m4t-v2-large", cache_dir=HUGGINGFACE_CACHE_DIR
+)
+logging.debug("Modèle chargé avec succès et mis en cache : %s", model)
+
 
 UPLOAD_FOLDER = "audio_files"
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)

huggingface_cache/models--facebook--seamless-m4t-v2-large/blobs/7472c1ede2bb52298c22c063a0ad071c420e1e97

@@ -0,0 +1,111 @@
+{
+  "feature_extractor_type": "SeamlessM4TFeatureExtractor",
+  "feature_size": 80,
+  "language_code": [
+    "__afr__",
+    "__amh__",
+    "__arb__",
+    "__ary__",
+    "__arz__",
+    "__asm__",
+    "__azj__",
+    "__bel__",
+    "__ben__",
+    "__bos__",
+    "__bul__",
+    "__cat__",
+    "__ceb__",
+    "__ces__",
+    "__ckb__",
+    "__cmn__",
+    "__cmn_Hant__",
+    "__cym__",
+    "__dan__",
+    "__deu__",
+    "__ell__",
+    "__eng__",
+    "__est__",
+    "__eus__",
+    "__fin__",
+    "__fra__",
+    "__fuv__",
+    "__gaz__",
+    "__gle__",
+    "__glg__",
+    "__guj__",
+    "__heb__",
+    "__hin__",
+    "__hrv__",
+    "__hun__",
+    "__hye__",
+    "__ibo__",
+    "__ind__",
+    "__isl__",
+    "__ita__",
+    "__jav__",
+    "__jpn__",
+    "__kan__",
+    "__kat__",
+    "__kaz__",
+    "__khk__",
+    "__khm__",
+    "__kir__",
+    "__kor__",
+    "__lao__",
+    "__lit__",
+    "__lug__",
+    "__luo__",
+    "__lvs__",
+    "__mai__",
+    "__mal__",
+    "__mar__",
+    "__mkd__",
+    "__mlt__",
+    "__mni__",
+    "__mya__",
+    "__nld__",
+    "__nno__",
+    "__nob__",
+    "__npi__",
+    "__nya__",
+    "__ory__",
+    "__pan__",
+    "__pbt__",
+    "__pes__",
+    "__pol__",
+    "__por__",
+    "__ron__",
+    "__rus__",
+    "__sat__",
+    "__slk__",
+    "__slv__",
+    "__sna__",
+    "__snd__",
+    "__som__",
+    "__spa__",
+    "__srp__",
+    "__swe__",
+    "__swh__",
+    "__tam__",
+    "__tel__",
+    "__tgk__",
+    "__tgl__",
+    "__tha__",
+    "__tur__",
+    "__ukr__",
+    "__urd__",
+    "__uzn__",
+    "__vie__",
+    "__yor__",
+    "__yue__",
+    "__zlm__",
+    "__zul__"
+  ],
+  "num_mel_bins": 80,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "SeamlessM4TProcessor",
+  "return_attention_mask": true,
+  "sampling_rate": 16000,
+  "stride": 2
+}

huggingface_cache/models--facebook--seamless-m4t-v2-large/refs/main

@@ -0,0 +1 @@
+5f8cc790b19fc3f67a61c105133b20b34e3dcb76

huggingface_cache/models--facebook--seamless-m4t-v2-large/snapshots/5f8cc790b19fc3f67a61c105133b20b34e3dcb76/preprocessor_config.json

@@ -0,0 +1 @@
+../../blobs/7472c1ede2bb52298c22c063a0ad071c420e1e97