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Update all modal deployments and change seamless configuration due to changes in src repo (#353)

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* update all modal deployments and change seamless configuration due to change in src repo

* add fixture
This commit is contained in:
projects-g
2024-04-16 21:12:24 +05:30
committed by GitHub
parent 32bb411652
commit 72b22d1005
2 changed files with 20 additions and 17 deletions
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35
server/gpu/modal/reflector_translator.py
View File

@@ -55,15 +55,15 @@ def configure_seamless_m4t():
import yaml
ASSETS_DIR: str = "./seamless_communication/src/seamless_communication/assets/cards"
CARDS_DIR: str = "./seamless_communication/src/seamless_communication/cards"
with open(f"{ASSETS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml", "r") as file:
with open(f"{CARDS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml", "r") as file:
model_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f"{ASSETS_DIR}/vocoder_36langs.yaml", "r") as file:
with open(f"{CARDS_DIR}/vocoder_36langs.yaml", "r") as file:
vocoder_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f"{ASSETS_DIR}/unity_nllb-100.yaml", "r") as file:
with open(f"{CARDS_DIR}/unity_nllb-100.yaml", "r") as file:
unity_100_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f"{ASSETS_DIR}/unity_nllb-200.yaml", "r") as file:
with open(f"{CARDS_DIR}/unity_nllb-200.yaml", "r") as file:
unity_200_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
model_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}/snapshots"
@@ -87,18 +87,18 @@ def configure_seamless_m4t():
os.getcwd(), model_dir, latest_model_version, tokenizer_name
)
model_yaml_data["checkpoint"] = f"file:/{model_path}"
vocoder_yaml_data["checkpoint"] = f"file:/{vocoder_path}"
unity_100_yaml_data["tokenizer"] = f"file:/{tokenizer_path}"
unity_200_yaml_data["tokenizer"] = f"file:/{tokenizer_path}"
model_yaml_data["checkpoint"] = f"file://{model_path}"
vocoder_yaml_data["checkpoint"] = f"file://{vocoder_path}"
unity_100_yaml_data["tokenizer"] = f"file://{tokenizer_path}"
unity_200_yaml_data["tokenizer"] = f"file://{tokenizer_path}"
with open(f"{ASSETS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml", "w") as file:
with open(f"{CARDS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml", "w") as file:
yaml.dump(model_yaml_data, file)
with open(f"{ASSETS_DIR}/vocoder_36langs.yaml", "w") as file:
with open(f"{CARDS_DIR}/vocoder_36langs.yaml", "w") as file:
yaml.dump(vocoder_yaml_data, file)
with open(f"{ASSETS_DIR}/unity_nllb-100.yaml", "w") as file:
with open(f"{CARDS_DIR}/unity_nllb-100.yaml", "w") as file:
yaml.dump(unity_100_yaml_data, file)
with open(f"{ASSETS_DIR}/unity_nllb-200.yaml", "w") as file:
with open(f"{CARDS_DIR}/unity_nllb-200.yaml", "w") as file:
yaml.dump(unity_200_yaml_data, file)
@@ -144,7 +144,7 @@ transcriber_image = (
class Translator:
def __enter__(self):
import torch
from seamless_communication.models.inference.translator import Translator
from seamless_communication.inference.translator import Translator
self.lock = threading.Lock()
self.use_gpu = torch.cuda.is_available()
@@ -363,14 +363,15 @@ class Translator:
@method()
def translate_text(self, text: str, source_language: str, target_language: str):
with self.lock:
translated_text, _, _ = self.translator.predict(
translation_result, _ = self.translator.predict(
text,
"t2tt",
src_lang=self.get_seamless_lang_code(source_language),
tgt_lang=self.get_seamless_lang_code(target_language),
ngram_filtering=True,
unit_generation_ngram_filtering=True,
)
return {"text": {source_language: text, target_language: str(translated_text)}}
translated_text = str(translation_result[0])
return {"text": {source_language: text, target_language: translated_text}}
# -------------------------------------------------------------------

2
server/tests/test_transcripts_rtc_ws.py
View File

@@ -70,6 +70,7 @@ async def test_transcript_rtc_and_websocket(
dummy_storage,
fake_mp3_upload,
ensure_casing,
nltk,
appserver,
sentence_tokenize,
):
@@ -227,6 +228,7 @@ async def test_transcript_rtc_and_websocket_and_fr(
dummy_storage,
fake_mp3_upload,
ensure_casing,
nltk,
appserver,
sentence_tokenize,
):