Merge branch 'main' of github.com:Monadical-SAS/reflector into sara/recorder-memory

.github/workflows/deploy.yml

@@ -43,3 +43,5 @@ jobs:
           platforms: linux/amd64,linux/arm64
           push: true
           tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

.github/workflows/test_server.yml

@@ -13,23 +13,14 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v3
+    - name: Install poetry
+      run: pipx install poetry
     - name: Set up Python 3.x
       uses: actions/setup-python@v4
       with:
-        python-version: 3.11
-    - uses: Gr1N/setup-poetry@v8
-    - name: Cache Python requirements
-      uses: actions/cache@v2
-      id: cache-pip
-      with:
-        path: ~/.cache/pypoetry/virtualenvs
-        key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }}
-        restore-keys: |
-          - ${{ runner.os }}-poetry-
-    - name: Install tests dependencies
-      run: |
-        sudo apt-get update
-        sudo apt-get install -y portaudio19-dev build-essential
+        python-version: '3.11'
+        cache: 'poetry'
+        cache-dependency-path: 'server/poetry.lock'
     - name: Install requirements
       run: |
         cd server
@@ -81,3 +72,5 @@ jobs:
       with:
         context: server
         platforms: linux/amd64,linux/arm64
+        cache-from: type=gha
+        cache-to: type=gha,mode=max

server/env.example

@@ -48,6 +48,7 @@
 ## Using serverless modal.com (require reflector-gpu-modal deployed)
 #TRANSCRIPT_BACKEND=modal
 #TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-web.modal.run
+#TRANSLATE_URL=https://xxxxx--reflector-translator-web.modal.run
 #TRANSCRIPT_MODAL_API_KEY=xxxxx
 
 ## Using serverless banana.dev (require reflector-gpu-banana deployed)

server/gpu/modal/reflector_transcriber.py

@@ -14,34 +14,12 @@ WHISPER_MODEL: str = "large-v2"
 WHISPER_COMPUTE_TYPE: str = "float16"
 WHISPER_NUM_WORKERS: int = 1
 
-# Seamless M4T
-SEAMLESSM4T_MODEL_SIZE: str = "medium"
-SEAMLESSM4T_MODEL_CARD_NAME: str = f"seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}"
-SEAMLESSM4T_VOCODER_CARD_NAME: str = "vocoder_36langs"
-
-HF_SEAMLESS_M4TEPO: str = f"facebook/seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}"
-HF_SEAMLESS_M4T_VOCODEREPO: str = "facebook/seamless-m4t-vocoder"
-
-SEAMLESS_GITEPO: str = "https://github.com/facebookresearch/seamless_communication.git"
-SEAMLESS_MODEL_DIR: str = "m4t"
 
 WHISPER_MODEL_DIR = "/root/transcription_models"
 
 stub = Stub(name="reflector-transcriber")
 
 
-def install_seamless_communication():
-    import os
-    import subprocess
-    initial_dir = os.getcwd()
-    subprocess.run(["ssh-keyscan", "-t", "rsa", "github.com", ">>", "~/.ssh/known_hosts"])
-    subprocess.run(["rm", "-rf", "seamless_communication"])
-    subprocess.run(["git", "clone", SEAMLESS_GITEPO, "." + "/seamless_communication"])
-    os.chdir("seamless_communication")
-    subprocess.run(["pip", "install", "-e", "."])
-    os.chdir(initial_dir)
-
-
 def download_whisper():
     from faster_whisper.utils import download_model
 
@@ -50,18 +28,6 @@ def download_whisper():
     print("Whisper model downloaded")
 
 
-def download_seamlessm4t_model():
-    from huggingface_hub import snapshot_download
-
-    print("Downloading Transcriber model & tokenizer")
-    snapshot_download(HF_SEAMLESS_M4TEPO, cache_dir=SEAMLESS_MODEL_DIR)
-    print("Transcriber model & tokenizer downloaded")
-
-    print("Downloading vocoder weights")
-    snapshot_download(HF_SEAMLESS_M4T_VOCODEREPO, cache_dir=SEAMLESS_MODEL_DIR)
-    print("Vocoder weights downloaded")
-
-
 def migrate_cache_llm():
     """
     XXX The cache for model files in Transformers v4.22.0 has been updated.
@@ -76,52 +42,6 @@ def migrate_cache_llm():
     print("LLM cache moved")
 
 
-def configure_seamless_m4t():
-    import os
-
-    import yaml
-
-    ASSETS_DIR: str = "./seamless_communication/src/seamless_communication/assets/cards"
-
-    with open(f'{ASSETS_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:
-        vocoder_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
-    with open(f'{ASSETS_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:
-        unity_200_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
-
-    model_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}/snapshots"
-    available_model_versions = os.listdir(model_dir)
-    latest_model_version = sorted(available_model_versions)[-1]
-    model_name = f"multitask_unity_{SEAMLESSM4T_MODEL_SIZE}.pt"
-    model_path = os.path.join(os.getcwd(), model_dir, latest_model_version, model_name)
-
-    vocoder_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-vocoder/snapshots"
-    available_vocoder_versions = os.listdir(vocoder_dir)
-    latest_vocoder_version = sorted(available_vocoder_versions)[-1]
-    vocoder_name = "vocoder_36langs.pt"
-    vocoder_path = os.path.join(os.getcwd(), vocoder_dir, latest_vocoder_version, vocoder_name)
-
-    tokenizer_name = "tokenizer.model"
-    tokenizer_path = os.path.join(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}"
-
-    with open(f'{ASSETS_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:
-        yaml.dump(vocoder_yaml_data, file)
-    with open(f'{ASSETS_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:
-        yaml.dump(unity_200_yaml_data, file)
-
-
 transcriber_image = (
     Image.debian_slim(python_version="3.10.8")
     .apt_install("git")
@@ -131,7 +51,7 @@ transcriber_image = (
         "faster-whisper",
         "requests",
         "torch",
-        "transformers",
+        "transformers==4.34.0",
         "sentencepiece",
         "protobuf",
         "huggingface_hub==0.16.4",
@@ -141,9 +61,6 @@ transcriber_image = (
         "pyyaml",
         "hf-transfer~=0.1"
     )
-    .run_function(install_seamless_communication)
-    .run_function(download_seamlessm4t_model)
-    .run_function(configure_seamless_m4t)
     .run_function(download_whisper)
     .run_function(migrate_cache_llm)
     .env(
@@ -167,7 +84,6 @@ class Transcriber:
     def __enter__(self):
         import faster_whisper
         import torch
-        from seamless_communication.models.inference.translator import Translator
 
         self.use_gpu = torch.cuda.is_available()
         self.device = "cuda" if self.use_gpu else "cpu"
@@ -178,12 +94,6 @@ class Transcriber:
             num_workers=WHISPER_NUM_WORKERS,
             download_root=WHISPER_MODEL_DIR
         )
-        self.translator = Translator(
-            SEAMLESSM4T_MODEL_CARD_NAME,
-            SEAMLESSM4T_VOCODER_CARD_NAME,
-            torch.device(self.device),
-            dtype=torch.float32
-        )
 
     @method()
     def transcribe_segment(
@@ -229,38 +139,6 @@ class Transcriber:
                 "words": words
             }
 
-    def get_seamless_lang_code(self, lang_code: str):
-        """
-        The codes for SeamlessM4T is different from regular standards.
-        For ex, French is "fra" and not "fr".
-        """
-        # TODO: Enhance with complete list of lang codes
-        seamless_lang_code = {
-            "en": "eng",
-            "fr": "fra"
-        }
-        return seamless_lang_code.get(lang_code, "eng")
-
-    @method()
-    def translate_text(
-            self,
-            text: str,
-            source_language: str,
-            target_language: str
-    ):
-        translated_text, _, _ = 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
-        )
-        return {
-            "text": {
-                source_language: text,
-                target_language: str(translated_text)
-            }
-        }
 # -------------------------------------------------------------------
 # Web API
 # -------------------------------------------------------------------
@@ -316,18 +194,4 @@ def web():
         result = func.get()
         return result
 
-    @app.post("/translate", dependencies=[Depends(apikey_auth)])
-    async def translate(
-            text: str,
-            source_language: Annotated[str, Body(...)] = "en",
-            target_language: Annotated[str, Body(...)] = "fr",
-    ) -> TranscriptResponse:
-        func = transcriberstub.translate_text.spawn(
-            text=text,
-            source_language=source_language,
-            target_language=target_language,
-        )
-        result = func.get()
-        return result
-
     return app

server/gpu/modal/reflector_translator.py

@@ -0,0 +1,237 @@
+"""
+Reflector GPU backend - transcriber
+===================================
+"""
+
+import os
+import tempfile
+
+from modal import Image, Secret, Stub, asgi_app, method
+from pydantic import BaseModel
+
+# Seamless M4T
+SEAMLESSM4T_MODEL_SIZE: str = "medium"
+SEAMLESSM4T_MODEL_CARD_NAME: str = f"seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}"
+SEAMLESSM4T_VOCODER_CARD_NAME: str = "vocoder_36langs"
+
+HF_SEAMLESS_M4TEPO: str = f"facebook/seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}"
+HF_SEAMLESS_M4T_VOCODEREPO: str = "facebook/seamless-m4t-vocoder"
+
+SEAMLESS_GITEPO: str = "https://github.com/facebookresearch/seamless_communication.git"
+SEAMLESS_MODEL_DIR: str = "m4t"
+
+stub = Stub(name="reflector-translator")
+
+
+def install_seamless_communication():
+    import os
+    import subprocess
+    initial_dir = os.getcwd()
+    subprocess.run(["ssh-keyscan", "-t", "rsa", "github.com", ">>", "~/.ssh/known_hosts"])
+    subprocess.run(["rm", "-rf", "seamless_communication"])
+    subprocess.run(["git", "clone", SEAMLESS_GITEPO, "." + "/seamless_communication"])
+    os.chdir("seamless_communication")
+    subprocess.run(["pip", "install", "-e", "."])
+    os.chdir(initial_dir)
+
+
+def download_seamlessm4t_model():
+    from huggingface_hub import snapshot_download
+
+    print("Downloading Transcriber model & tokenizer")
+    snapshot_download(HF_SEAMLESS_M4TEPO, cache_dir=SEAMLESS_MODEL_DIR)
+    print("Transcriber model & tokenizer downloaded")
+
+    print("Downloading vocoder weights")
+    snapshot_download(HF_SEAMLESS_M4T_VOCODEREPO, cache_dir=SEAMLESS_MODEL_DIR)
+    print("Vocoder weights downloaded")
+
+
+def configure_seamless_m4t():
+    import os
+
+    import yaml
+
+    ASSETS_DIR: str = "./seamless_communication/src/seamless_communication/assets/cards"
+
+    with open(f'{ASSETS_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:
+        vocoder_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
+    with open(f'{ASSETS_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:
+        unity_200_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
+
+    model_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}/snapshots"
+    available_model_versions = os.listdir(model_dir)
+    latest_model_version = sorted(available_model_versions)[-1]
+    model_name = f"multitask_unity_{SEAMLESSM4T_MODEL_SIZE}.pt"
+    model_path = os.path.join(os.getcwd(), model_dir, latest_model_version, model_name)
+
+    vocoder_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-vocoder/snapshots"
+    available_vocoder_versions = os.listdir(vocoder_dir)
+    latest_vocoder_version = sorted(available_vocoder_versions)[-1]
+    vocoder_name = "vocoder_36langs.pt"
+    vocoder_path = os.path.join(os.getcwd(), vocoder_dir, latest_vocoder_version, vocoder_name)
+
+    tokenizer_name = "tokenizer.model"
+    tokenizer_path = os.path.join(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}"
+
+    with open(f'{ASSETS_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:
+        yaml.dump(vocoder_yaml_data, file)
+    with open(f'{ASSETS_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:
+        yaml.dump(unity_200_yaml_data, file)
+
+
+transcriber_image = (
+    Image.debian_slim(python_version="3.10.8")
+    .apt_install("git")
+    .apt_install("wget")
+    .apt_install("libsndfile-dev")
+    .pip_install(
+        "requests",
+        "torch",
+        "transformers==4.34.0",
+        "sentencepiece",
+        "protobuf",
+        "huggingface_hub==0.16.4",
+        "gitpython",
+        "torchaudio",
+        "fairseq2",
+        "pyyaml",
+        "hf-transfer~=0.1"
+    )
+    .run_function(install_seamless_communication)
+    .run_function(download_seamlessm4t_model)
+    .run_function(configure_seamless_m4t)
+    .env(
+        {
+            "LD_LIBRARY_PATH": (
+                "/usr/local/lib/python3.10/site-packages/nvidia/cudnn/lib/:"
+                "/opt/conda/lib/python3.10/site-packages/nvidia/cublas/lib/"
+            )
+        }
+    )
+)
+
+
+@stub.cls(
+    gpu="A10G",
+    timeout=60 * 5,
+    container_idle_timeout=60 * 5,
+    image=transcriber_image,
+)
+class Translator:
+    def __enter__(self):
+        import torch
+        from seamless_communication.models.inference.translator import Translator
+
+        self.use_gpu = torch.cuda.is_available()
+        self.device = "cuda" if self.use_gpu else "cpu"
+        self.translator = Translator(
+            SEAMLESSM4T_MODEL_CARD_NAME,
+            SEAMLESSM4T_VOCODER_CARD_NAME,
+            torch.device(self.device),
+            dtype=torch.float32
+        )
+
+    @method()
+    def warmup(self):
+        return {"status": "ok"}
+
+    def get_seamless_lang_code(self, lang_code: str):
+        """
+        The codes for SeamlessM4T is different from regular standards.
+        For ex, French is "fra" and not "fr".
+        """
+        # TODO: Enhance with complete list of lang codes
+        seamless_lang_code = {
+            "en": "eng",
+            "fr": "fra"
+        }
+        return seamless_lang_code.get(lang_code, "eng")
+
+    @method()
+    def translate_text(
+            self,
+            text: str,
+            source_language: str,
+            target_language: str
+    ):
+        translated_text, _, _ = 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
+        )
+        return {
+            "text": {
+                source_language: text,
+                target_language: str(translated_text)
+            }
+        }
+# -------------------------------------------------------------------
+# Web API
+# -------------------------------------------------------------------
+
+
+@stub.function(
+    container_idle_timeout=60,
+    timeout=60,
+    secrets=[
+        Secret.from_name("reflector-gpu"),
+    ],
+)
+@asgi_app()
+def web():
+    from fastapi import Body, Depends, FastAPI, HTTPException, status
+    from fastapi.security import OAuth2PasswordBearer
+    from typing_extensions import Annotated
+
+    translatorstub = Translator()
+
+    app = FastAPI()
+
+    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
+        if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
+            raise HTTPException(
+                status_code=status.HTTP_401_UNAUTHORIZED,
+                detail="Invalid API key",
+                headers={"WWW-Authenticate": "Bearer"},
+            )
+
+    class TranslateResponse(BaseModel):
+        result: dict
+
+    @app.post("/translate", dependencies=[Depends(apikey_auth)])
+    async def translate(
+            text: str,
+            source_language: Annotated[str, Body(...)] = "en",
+            target_language: Annotated[str, Body(...)] = "fr",
+    ) -> TranslateResponse:
+        func = translatorstub.translate_text.spawn(
+            text=text,
+            source_language=source_language,
+            target_language=target_language,
+        )
+        result = func.get()
+        return result
+
+    @app.post("/warmup", dependencies=[Depends(apikey_auth)])
+    async def warmup():
+        return translatorstub.warmup.spawn().get()
+
+    return app

server/poetry.lock

@@ -2173,29 +2173,6 @@ files = [
     {file = "protobuf-4.24.4.tar.gz", hash = "sha256:5a70731910cd9104762161719c3d883c960151eea077134458503723b60e3667"},
 ]
 
-[[package]]
-name = "pyaudio"
-version = "0.2.13"
-description = "Cross-platform audio I/O with PortAudio"
-optional = false
-python-versions = "*"
-files = [
-    {file = "PyAudio-0.2.13-cp310-cp310-win32.whl", hash = "sha256:48e29537ea22ae2ae323eebe297bfb2683831cee4f20d96964e131f65ab2161d"},
-    {file = "PyAudio-0.2.13-cp310-cp310-win_amd64.whl", hash = "sha256:87137cfd0ef8608a2a383be3f6996f59505e322dab9d16531f14cf542fa294f1"},
-    {file = "PyAudio-0.2.13-cp311-cp311-win32.whl", hash = "sha256:13915faaa780e6bbbb6d745ef0e761674fd461b1b1b3f9c1f57042a534bfc0c3"},
-    {file = "PyAudio-0.2.13-cp311-cp311-win_amd64.whl", hash = "sha256:59cc3cc5211b729c7854e3989058a145872cc58b1a7b46c6d4d88448a343d890"},
-    {file = "PyAudio-0.2.13-cp37-cp37m-win32.whl", hash = "sha256:d294e3f85b2238649b1ff49ce3412459a8a312569975a89d14646536362d7576"},
-    {file = "PyAudio-0.2.13-cp37-cp37m-win_amd64.whl", hash = "sha256:ff7f5e44ef51fe61da1e09c6f632f0b5808198edd61b363855cc7dd03bf4a8ac"},
-    {file = "PyAudio-0.2.13-cp38-cp38-win32.whl", hash = "sha256:c6b302b048c054b7463936d8ba884b73877dc47012f3c94665dba92dd658ae04"},
-    {file = "PyAudio-0.2.13-cp38-cp38-win_amd64.whl", hash = "sha256:1505d766ee718df6f5a18b73ac42307ba1cb4d2c0397873159254a34f67515d6"},
-    {file = "PyAudio-0.2.13-cp39-cp39-win32.whl", hash = "sha256:eb128e4a6ea9b98d9a31f33c44978885af27dbe8ae53d665f8790cbfe045517e"},
-    {file = "PyAudio-0.2.13-cp39-cp39-win_amd64.whl", hash = "sha256:910ef09225cce227adbba92622d4a3e3c8375117f7dd64039f287d9ffc0e02a1"},
-    {file = "PyAudio-0.2.13.tar.gz", hash = "sha256:26bccc81e4243d1c0ff5487e6b481de6329fcd65c79365c267cef38f363a2b56"},
-]
-
-[package.extras]
-test = ["numpy"]
-
 [[package]]
 name = "pycparser"
 version = "2.21"
@@ -3861,4 +3838,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "a85cb09a0e4b68b29c4272d550e618d2e24ace5f16b707f29e8ac4ce915c1fae"
+content-hash = "61578467a70980ff9c2dc0cd787b6410b91d7c5fd2bb4c46b6951ec82690ef67"

server/pyproject.toml

@@ -40,10 +40,6 @@ black = "^23.7.0"
 stamina = "^23.1.0"
 
 
-[tool.poetry.group.client.dependencies]
-pyaudio = "^0.2.13"
-
-
 [tool.poetry.group.tests.dependencies]
 pytest-cov = "^4.1.0"
 pytest-aiohttp = "^1.0.4"

server/reflector/processors/transcript_translator.py

@@ -16,8 +16,8 @@ class TranscriptTranslatorProcessor(Processor):
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.transcript_url = settings.TRANSCRIPT_URL
-        self.timeout = settings.TRANSCRIPT_TIMEOUT
+        self.translate_url = settings.TRANSLATE_URL
+        self.timeout = settings.TRANSLATE_TIMEOUT
         self.headers = {"Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}"}
 
     async def _push(self, data: Transcript):
@@ -46,7 +46,7 @@ class TranscriptTranslatorProcessor(Processor):
 
         async with httpx.AsyncClient() as client:
             response = await retry(client.post)(
-                settings.TRANSCRIPT_URL + "/translate",
+                self.translate_url + "/translate",
                 headers=self.headers,
                 params=json_payload,
                 timeout=self.timeout,

server/reflector/settings.py

@@ -38,6 +38,10 @@ class Settings(BaseSettings):
     TRANSCRIPT_URL: str | None = None
     TRANSCRIPT_TIMEOUT: int = 90
 
+    # Translate into the target language
+    TRANSLATE_URL: str | None = None
+    TRANSLATE_TIMEOUT: int = 90
+
     # Audio transcription banana.dev configuration
     TRANSCRIPT_BANANA_API_KEY: str | None = None
     TRANSCRIPT_BANANA_MODEL_KEY: str | None = None

server/reflector/views/_range_requests_response.py

@@ -38,7 +38,9 @@ def _get_range_header(range_header: str, file_size: int) -> tuple[int, int]:
     return start, end
 
 
-def range_requests_response(request: Request, file_path: str, content_type: str):
+def range_requests_response(
+    request: Request, file_path: str, content_type: str, content_disposition: str
+):
     """Returns StreamingResponse using Range Requests of a given file"""
 
     file_size = os.stat(file_path).st_size
@@ -54,6 +56,10 @@ def range_requests_response(request: Request, file_path: str, content_type: str)
             "content-range, content-encoding"
         ),
     }
+
+    if content_disposition:
+        headers["Content-Disposition"] = content_disposition
+
     start = 0
     end = file_size - 1
     status_code = status.HTTP_200_OK

server/reflector/views/transcripts.py

@@ -356,10 +356,14 @@ async def transcript_get_audio_mp3(
     if not transcript.audio_mp3_filename.exists():
         raise HTTPException(status_code=404, detail="Audio not found")
 
+    truncated_id = str(transcript.id).split("-")[0]
+    filename = f"recording_{truncated_id}.mp3"
+
     return range_requests_response(
         request,
         transcript.audio_mp3_filename,
-        content_type="audio/mp3",
+        content_type="audio/mpeg",
+        content_disposition=f"attachment; filename={filename}",
     )
 
 

server/tests/test_transcripts_audio_download.py

@@ -35,7 +35,7 @@ async def fake_transcript(tmpdir):
 @pytest.mark.parametrize(
     "url_suffix,content_type",
     [
-        ["/mp3", "audio/mp3"],
+        ["/mp3", "audio/mpeg"],
     ],
 )
 async def test_transcript_audio_download(fake_transcript, url_suffix, content_type):
@@ -51,7 +51,7 @@ async def test_transcript_audio_download(fake_transcript, url_suffix, content_ty
 @pytest.mark.parametrize(
     "url_suffix,content_type",
     [
-        ["/mp3", "audio/mp3"],
+        ["/mp3", "audio/mpeg"],
     ],
 )
 async def test_transcript_audio_download_range(
@@ -74,7 +74,7 @@ async def test_transcript_audio_download_range(
 @pytest.mark.parametrize(
     "url_suffix,content_type",
     [
-        ["/mp3", "audio/mp3"],
+        ["/mp3", "audio/mpeg"],
     ],
 )
 async def test_transcript_audio_download_range_with_seek(

server/tests/test_transcripts_rtc_ws.py

@@ -31,28 +31,43 @@ class ThreadedUvicorn:
                 continue
 
 
-@pytest.mark.asyncio
-async def test_transcript_rtc_and_websocket(
-    tmpdir, dummy_llm, dummy_transcript, dummy_processors, ensure_casing
-):
-    # goal: start the server, exchange RTC, receive websocket events
-    # because of that, we need to start the server in a thread
-    # to be able to connect with aiortc
-
+@pytest.fixture
+async def appserver(tmpdir):
     from reflector.settings import settings
     from reflector.app import app
 
+    DATA_DIR = settings.DATA_DIR
     settings.DATA_DIR = Path(tmpdir)
 
     # start server
     host = "127.0.0.1"
     port = 1255
-    base_url = f"http://{host}:{port}/v1"
     config = Config(app=app, host=host, port=port)
     server = ThreadedUvicorn(config)
     await server.start()
 
+    yield (server, host, port)
+
+    server.stop()
+    settings.DATA_DIR = DATA_DIR
+
+
+@pytest.mark.asyncio
+async def test_transcript_rtc_and_websocket(
+    tmpdir,
+    dummy_llm,
+    dummy_transcript,
+    dummy_processors,
+    ensure_casing,
+    appserver,
+):
+    # goal: start the server, exchange RTC, receive websocket events
+    # because of that, we need to start the server in a thread
+    # to be able to connect with aiortc
+    server, host, port = appserver
+
     # create a transcript
+    base_url = f"http://{host}:{port}/v1"
     ac = AsyncClient(base_url=base_url)
     response = await ac.post("/transcripts", json={"name": "Test RTC"})
     assert response.status_code == 200
@@ -167,35 +182,26 @@ async def test_transcript_rtc_and_websocket(
     # check that audio/mp3 is available
     resp = await ac.get(f"/transcripts/{tid}/audio/mp3")
     assert resp.status_code == 200
-    assert resp.headers["Content-Type"] == "audio/mp3"
-
-    # stop server
-    server.stop()
+    assert resp.headers["Content-Type"] == "audio/mpeg"
 
 
 @pytest.mark.asyncio
 async def test_transcript_rtc_and_websocket_and_fr(
-    tmpdir, dummy_llm, dummy_transcript, dummy_processors, ensure_casing
+    tmpdir,
+    dummy_llm,
+    dummy_transcript,
+    dummy_processors,
+    ensure_casing,
+    appserver,
 ):
     # goal: start the server, exchange RTC, receive websocket events
     # because of that, we need to start the server in a thread
     # to be able to connect with aiortc
     # with target french language
-
-    from reflector.settings import settings
-    from reflector.app import app
-
-    settings.DATA_DIR = Path(tmpdir)
-
-    # start server
-    host = "127.0.0.1"
-    port = 1255
-    base_url = f"http://{host}:{port}/v1"
-    config = Config(app=app, host=host, port=port)
-    server = ThreadedUvicorn(config)
-    await server.start()
+    server, host, port = appserver
 
     # create a transcript
+    base_url = f"http://{host}:{port}/v1"
     ac = AsyncClient(base_url=base_url)
     response = await ac.post(
         "/transcripts", json={"name": "Test RTC", "target_language": "fr"}
@@ -303,6 +309,3 @@ async def test_transcript_rtc_and_websocket_and_fr(
     # ensure the last event received is ended
     assert events[-1]["event"] == "STATUS"
     assert events[-1]["data"]["value"] == "ended"
-
-    # stop server
-    server.stop()

www/app/api/apis/DefaultApi.ts

@@ -54,10 +54,6 @@ export interface V1TranscriptGetRequest {
   transcriptId: any;
 }
 
-export interface V1TranscriptGetAudioRequest {
-  transcriptId: any;
-}
-
 export interface V1TranscriptGetAudioMp3Request {
   transcriptId: any;
 }
@@ -310,69 +306,6 @@ export class DefaultApi extends runtime.BaseAPI {
     return await response.value();
   }
 
-  /**
-   * Transcript Get Audio
-   */
-  async v1TranscriptGetAudioRaw(
-    requestParameters: V1TranscriptGetAudioRequest,
-    initOverrides?: RequestInit | runtime.InitOverrideFunction,
-  ): Promise<runtime.ApiResponse<any>> {
-    if (
-      requestParameters.transcriptId === null ||
-      requestParameters.transcriptId === undefined
-    ) {
-      throw new runtime.RequiredError(
-        "transcriptId",
-        "Required parameter requestParameters.transcriptId was null or undefined when calling v1TranscriptGetAudio.",
-      );
-    }
-
-    const queryParameters: any = {};
-
-    const headerParameters: runtime.HTTPHeaders = {};
-
-    if (this.configuration && this.configuration.accessToken) {
-      // oauth required
-      headerParameters["Authorization"] = await this.configuration.accessToken(
-        "OAuth2AuthorizationCodeBearer",
-        [],
-      );
-    }
-
-    const response = await this.request(
-      {
-        path: `/v1/transcripts/{transcript_id}/audio`.replace(
-          `{${"transcript_id"}}`,
-          encodeURIComponent(String(requestParameters.transcriptId)),
-        ),
-        method: "GET",
-        headers: headerParameters,
-        query: queryParameters,
-      },
-      initOverrides,
-    );
-
-    if (this.isJsonMime(response.headers.get("content-type"))) {
-      return new runtime.JSONApiResponse<any>(response);
-    } else {
-      return new runtime.TextApiResponse(response) as any;
-    }
-  }
-
-  /**
-   * Transcript Get Audio
-   */
-  async v1TranscriptGetAudio(
-    requestParameters: V1TranscriptGetAudioRequest,
-    initOverrides?: RequestInit | runtime.InitOverrideFunction,
-  ): Promise<any> {
-    const response = await this.v1TranscriptGetAudioRaw(
-      requestParameters,
-      initOverrides,
-    );
-    return await response.value();
-  }
-
   /**
    * Transcript Get Audio Mp3
    */

www/app/transcripts/[transcriptId]/page.tsx

@@ -12,6 +12,7 @@ import "../../styles/button.css";
 import FinalSummary from "../finalSummary";
 import ShareLink from "../shareLink";
 import QRCode from "react-qr-code";
+import TranscriptTitle from "../transcriptTitle";
 
 type TranscriptDetails = {
   params: {
@@ -50,13 +51,18 @@ export default function TranscriptDetails(details: TranscriptDetails) {
         <Modal title="Loading" text={"Loading transcript..."} />
       ) : (
         <>
-          <Recorder
-            topics={topics?.topics || []}
-            useActiveTopic={useActiveTopic}
-            waveform={waveform?.waveform}
-            isPastMeeting={true}
-            transcriptId={transcript?.response?.id}
-          />
+          <div className="flex flex-col">
+            {transcript?.response?.title && (
+              <TranscriptTitle title={transcript.response.title} />
+            )}
+            <Recorder
+              topics={topics?.topics || []}
+              useActiveTopic={useActiveTopic}
+              waveform={waveform?.waveform}
+              isPastMeeting={true}
+              transcriptId={transcript?.response?.id}
+            />
+          </div>
           <div className="grid grid-cols-1 lg:grid-cols-2 grid-rows-2 lg:grid-rows-1 gap-2 lg:gap-4 h-full">
             <TopicList
               topics={topics?.topics || []}

www/app/transcripts/recorder.tsx

@@ -301,6 +301,9 @@ export default function Recorder(props: RecorderProps) {
             <a
               title="Download recording"
               className="text-center cursor-pointer text-blue-400 hover:text-blue-700 ml-2 md:ml:4 p-2 rounded-lg outline-blue-400"
+              download={`recording-${
+                props.transcriptId?.split("-")[0] || "0000"
+              }`}
               href={`${process.env.NEXT_PUBLIC_API_URL}/v1/transcripts/${props.transcriptId}/audio/mp3`}
             >
               <FontAwesomeIcon icon={faDownload} className="h-5 w-auto" />

www/app/transcripts/transcriptTitle.tsx

@@ -0,0 +1,13 @@
+type TranscriptTitle = {
+  title: string;
+};
+
+const TranscriptTitle = (props: TranscriptTitle) => {
+  return (
+    <h2 className="text-2xl lg:text-4xl font-extrabold text-center mb-4">
+      {props.title}
+    </h2>
+  );
+};
+
+export default TranscriptTitle;