its coqui-tts not coqui.

cpu_training_pipeline.py

@@ -65,11 +65,12 @@ def fetch_pdfs_from_s3(
 
     client = boto3.client(
         "s3",
-        endpoint_url=f"http://{s3_endpoint}",
+        endpoint_url=s3_endpoint,
         aws_access_key_id=aws_access_key_id,
         aws_secret_access_key=aws_secret_access_key,
         region_name="us-east-1",
         config=Config(signature_version="s3v4"),
+        verify=False,
     )
 
     paginator = client.get_paginator("list_objects_v2")
@@ -226,11 +227,12 @@ def upload_data_to_s3(
 
     client = boto3.client(
         "s3",
-        endpoint_url=f"http://{s3_endpoint}",
+        endpoint_url=s3_endpoint,
         aws_access_key_id=aws_access_key_id,
         aws_secret_access_key=aws_secret_access_key,
         region_name="us-east-1",
         config=Config(signature_version="s3v4"),
+        verify=False,
     )
 
     for fname in ["train.json", "val.json"]:
@@ -273,11 +275,12 @@ from trl import SFTTrainer
 def _s3_client(cfg):
     return boto3.client(
         "s3",
-        endpoint_url=f"http://{cfg['s3_endpoint']}",
+        endpoint_url=cfg['s3_endpoint'],
         aws_access_key_id=cfg["aws_access_key_id"],
         aws_secret_access_key=cfg["aws_secret_access_key"],
         region_name="us-east-1",
         config=Config(signature_version="s3v4"),
+        verify=False,
     )
 
 
@@ -693,11 +696,12 @@ def submit_ray_training_job(
     # ── Read results from S3 ────────────────────────────────
     s3 = boto3.client(
         "s3",
-        endpoint_url=f"http://{s3_endpoint}",
+        endpoint_url=s3_endpoint,
         aws_access_key_id=aws_access_key_id,
         aws_secret_access_key=aws_secret_access_key,
         region_name="us-east-1",
         config=Config(signature_version="s3v4"),
+        verify=False,
     )
 
     results_obj = s3.get_object(
@@ -751,11 +755,12 @@ def download_adapter_from_s3(
 
     client = boto3.client(
         "s3",
-        endpoint_url=f"http://{s3_endpoint}",
+        endpoint_url=s3_endpoint,
         aws_access_key_id=aws_access_key_id,
         aws_secret_access_key=aws_secret_access_key,
         region_name="us-east-1",
         config=Config(signature_version="s3v4"),
+        verify=False,
     )
 
     adapter_path = "/tmp/adapter"
@@ -1019,7 +1024,7 @@ def log_training_metrics(
 )
 def cpu_training_pipeline(
     # ── S3 / Quobjects ──
-    s3_endpoint: str = "candlekeep.lab.daviestechlabs.io",
+    s3_endpoint: str = "https://gravenhollow.lab.daviestechlabs.io:30292",
     s3_bucket: str = "training-data",
     s3_prefix: str = "",
     aws_access_key_id: str = "",

qlora_pdf_pipeline.py

@@ -46,11 +46,12 @@ def fetch_pdfs_from_s3(
 
     client = boto3.client(
         "s3",
-        endpoint_url=f"http://{s3_endpoint}",
+        endpoint_url=s3_endpoint,
         aws_access_key_id=aws_access_key_id,
         aws_secret_access_key=aws_secret_access_key,
         region_name="us-east-1",
         config=Config(signature_version="s3v4"),
+        verify=False,
     )
 
     paginator = client.get_paginator("list_objects_v2")
@@ -597,7 +598,7 @@ def log_training_metrics(
 )
 def qlora_pdf_pipeline(
     # ── S3 / Quobjects ──
-    s3_endpoint: str = "candlekeep.lab.daviestechlabs.io",
+    s3_endpoint: str = "https://gravenhollow.lab.daviestechlabs.io:30292",
     s3_bucket: str = "training-data",
     s3_prefix: str = "",
     aws_access_key_id: str = "",

voice_cloning_pipeline.py

@@ -47,10 +47,11 @@ def transcribe_and_diarise(
 
     client = boto3.client(
         "s3",
-        endpoint_url=f"http://{s3_endpoint}",
+        endpoint_url=s3_endpoint,
         aws_access_key_id="",
         aws_secret_access_key="",
         config=boto3.session.Config(signature_version="UNSIGNED"),
+        verify=False,
     )
     print(f"Downloading s3://{s3_bucket}/{s3_key} from {s3_endpoint}")
     client.download_file(s3_bucket, s3_key, audio_path)
@@ -258,7 +259,7 @@ def prepare_ljspeech_dataset(
 # 4.  Fine-tune Coqui VITS voice model
 # ──────────────────────────────────────────────────────────────
 @dsl.component(
-    base_image="ghcr.io/coqui-ai/tts:latest",
+    base_image="ghcr.io/idiap/coqui-tts:latest",
     packages_to_install=[],
 )
 def train_vits_voice(
@@ -585,7 +586,7 @@ def log_training_metrics(
     ),
 )
 def voice_cloning_pipeline(
-    s3_endpoint: str = "candlekeep.lab.daviestechlabs.io",
+    s3_endpoint: str = "https://gravenhollow.lab.daviestechlabs.io:30292",
     s3_bucket: str = "training-data",
     s3_key: str = "",
     target_speaker: str = "SPEAKER_0",

voice_cloning_pipeline.yaml

@@ -547,7 +547,7 @@ deploymentSpec:
           \ complete. Best checkpoint: {best_checkpoint}\")\n    print(f\"Final loss:\
           \ {final_loss:.4f}\")\n\n    return out(model_dir=OUTPUT_DIR, best_checkpoint=best_checkpoint,\
           \ final_loss=final_loss)\n\n"
-        image: ghcr.io/coqui-ai/tts:latest
+        image: ghcr.io/idiap/coqui-tts:latest
         resources:
           accelerator:
             resourceCount: '1'