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2024-12-22
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import enum
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings, SettingsConfigDict
SAMPLES_PER_SECOND = 16000
BYTES_PER_SAMPLE = 2
BYTES_PER_SECOND = SAMPLES_PER_SECOND * BYTES_PER_SAMPLE
# 2 BYTES = 16 BITS = 1 SAMPLE
# 1 SECOND OF AUDIO = 32000 BYTES = 16000 SAMPLES
# https://platform.openai.com/docs/api-reference/audio/createTranscription#audio-createtranscription-response_format
class ResponseFormat(enum.StrEnum):
TEXT = "text"
JSON = "json"
VERBOSE_JSON = "verbose_json"
SRT = "srt"
VTT = "vtt"
class Device(enum.StrEnum):
CPU = "cpu"
CUDA = "cuda"
AUTO = "auto"
# https://github.com/OpenNMT/CTranslate2/blob/master/docs/quantization.md
class Quantization(enum.StrEnum):
INT8 = "int8"
INT8_FLOAT16 = "int8_float16"
INT8_BFLOAT16 = "int8_bfloat16"
INT8_FLOAT32 = "int8_float32"
INT16 = "int16"
FLOAT16 = "float16"
BFLOAT16 = "bfloat16"
FLOAT32 = "float32"
DEFAULT = "default"
class Language(enum.StrEnum):
AF = "af"
AM = "am"
AR = "ar"
AS = "as"
AZ = "az"
BA = "ba"
BE = "be"
BG = "bg"
BN = "bn"
BO = "bo"
BR = "br"
BS = "bs"
CA = "ca"
CS = "cs"
CY = "cy"
DA = "da"
DE = "de"
EL = "el"
EN = "en"
ES = "es"
ET = "et"
EU = "eu"
FA = "fa"
FI = "fi"
FO = "fo"
FR = "fr"
GL = "gl"
GU = "gu"
HA = "ha"
HAW = "haw"
HE = "he"
HI = "hi"
HR = "hr"
HT = "ht"
HU = "hu"
HY = "hy"
ID = "id"
IS = "is"
IT = "it"
JA = "ja"
JW = "jw"
KA = "ka"
KK = "kk"
KM = "km"
KN = "kn"
KO = "ko"
LA = "la"
LB = "lb"
LN = "ln"
LO = "lo"
LT = "lt"
LV = "lv"
MG = "mg"
MI = "mi"
MK = "mk"
ML = "ml"
MN = "mn"
MR = "mr"
MS = "ms"
MT = "mt"
MY = "my"
NE = "ne"
NL = "nl"
NN = "nn"
NO = "no"
OC = "oc"
PA = "pa"
PL = "pl"
PS = "ps"
PT = "pt"
RO = "ro"
RU = "ru"
SA = "sa"
SD = "sd"
SI = "si"
SK = "sk"
SL = "sl"
SN = "sn"
SO = "so"
SQ = "sq"
SR = "sr"
SU = "su"
SV = "sv"
SW = "sw"
TA = "ta"
TE = "te"
TG = "tg"
TH = "th"
TK = "tk"
TL = "tl"
TR = "tr"
TT = "tt"
UK = "uk"
UR = "ur"
UZ = "uz"
VI = "vi"
YI = "yi"
YO = "yo"
YUE = "yue"
ZH = "zh"
class Task(enum.StrEnum):
TRANSCRIBE = "transcribe"
TRANSLATE = "translate"
class WhisperConfig(BaseModel):
"""See https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/transcribe.py#L599."""
model: str = Field(default="Systran/faster-whisper-small")
"""
Default Huggingface model to use for transcription. Note, the model must support being ran using CTranslate2.
This model will be used if no model is specified in the request.
Models created by authors of `faster-whisper` can be found at https://huggingface.co/Systran
You can find other supported models at https://huggingface.co/models?p=2&sort=trending&search=ctranslate2 and https://huggingface.co/models?sort=trending&search=ct2
"""
inference_device: Device = Field(default=Device.AUTO)
device_index: int | list[int] = 0
compute_type: Quantization = Field(default=Quantization.DEFAULT)
cpu_threads: int = 0
num_workers: int = 1
ttl: int = Field(default=300, ge=-1)
"""
Time in seconds until the model is unloaded if it is not being used.
-1: Never unload the model.
0: Unload the model immediately after usage.
"""
use_batched_mode: bool = False
"""
Whether to use batch mode(introduced in 1.1.0 `faster-whisper` release) for inference. This will likely become the default in the future and the configuration option will be removed.
""" # noqa: E501
class Config(BaseSettings):
"""Configuration for the application. Values can be set via environment variables.
Pydantic will automatically handle mapping uppercased environment variables to the corresponding fields.
To populate nested, the environment should be prefixed with the nested field name and an underscore. For example,
the environment variable `LOG_LEVEL` will be mapped to `log_level`, `WHISPER__MODEL`(note the double underscore) to `whisper.model`, to set quantization to int8, use `WHISPER__COMPUTE_TYPE=int8`, etc.
""" # noqa: E501
model_config = SettingsConfigDict(env_nested_delimiter="__")
api_key: str | None = None
log_level: str = "debug"
host: str = Field(alias="UVICORN_HOST", default="0.0.0.0")
port: int = Field(alias="UVICORN_PORT", default=8000)
allow_origins: list[str] | None = None
"""
https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values
Usage:
`export ALLOW_ORIGINS='["http://localhost:3000", "http://localhost:3001"]'`
`export ALLOW_ORIGINS='["*"]'`
"""
enable_ui: bool = True
"""
Whether to enable the Gradio UI. You may want to disable this if you want to minimize the dependencies.
"""
default_language: Language | None = None
"""
Default language to use for transcription. If not set, the language will be detected automatically.
It is recommended to set this as it will improve the performance.
"""
default_response_format: ResponseFormat = ResponseFormat.JSON
whisper: WhisperConfig = WhisperConfig()
preload_models: list[str] = Field(
default_factory=list,
examples=[
["Systran/faster-whisper-small"],
["Systran/faster-whisper-medium.en", "Systran/faster-whisper-small.en"],
],
)
"""
List of models to preload on startup. By default, the model is first loaded on first request.
"""
max_no_data_seconds: float = 1.0
"""
Max duration to wait for the next audio chunk before transcription is finilized and connection is closed.
"""
min_duration: float = 1.0
"""
Minimum duration of an audio chunk that will be transcribed.
"""
word_timestamp_error_margin: float = 0.2
max_inactivity_seconds: float = 2.5
"""
Max allowed audio duration without any speech being detected before transcription is finilized and connection is closed.
""" # noqa: E501
inactivity_window_seconds: float = 5.0
"""
Controls how many latest seconds of audio are being passed through VAD.
Should be greater than `max_inactivity_seconds`
"""
chat_completion_base_url: str = "https://api.openai.com/v1"
chat_completion_api_key: str | None = None
speech_base_url: str | None = None
speech_api_key: str | None = None
speech_model: str = "piper"
speech_extra_body: dict = {"sample_rate": 24000}
transcription_base_url: str | None = None
transcription_api_key: str | None = None