faster_whisper_server/config.py

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-medium.en")
    """
    Huggingface model to use for transcription. Note, the model must support being ran using CTranslate2.
    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


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`, etc.
    """  # noqa: E501

    model_config = SettingsConfigDict(env_nested_delimiter="__")

    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()
    max_models: int = 1
    """
    Maximum number of models that can be loaded at a time.
    """
    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`
    """


config = Config()