o
    %iS                     @  s   d Z ddlmZ ddlZddlZddlZddlmZ ddlm	Z	 ddl
Z
ddlm  mZ ddlmZmZ ddlmZmZmZ eeZed	Zd
ZG dd dZdS )z
MMS LID-256 (Meta): Language identification on 256 languages.
Architecture: Wav2Vec2, 1B params.  ~2GB fp16 VRAM.
Returns per-segment: top language (ISO-3), confidence, top-3 predictions.

Model loaded from R2 (no HuggingFace network calls at runtime).
    )annotationsN)Path)Optional)AutoFeatureExtractor!Wav2Vec2ForSequenceClassification   )MMS_LID_MODELMMS_BATCH_SIZEISO3_TO_LANGz/tmp/mms-lid-256zmodels/mms-lid-256.tarc                   @  sd   e Zd ZdZdejfdddZddddZed ddZ	dd Z
e 	d!d"ddZd"ddZdS )#MMSLIDz%Batch LID inference with MMS LID-256.cudadevicestrdtypetorch.dtypec                 C  s(   || _ || _d | _d | _i | _t| _d S N)r   r   model	processor	_id2labelr	   
batch_size)selfr   r    r   6/home/ubuntu/transcripts/validations/models/mms_lid.py__init__   s   
zMMSLID.__init__ hf_tokenc                 C  s   t d| j d| j d |  }tj|dd| _tj|| jdd	| j
 | _| jjj| _tdd | j D d	 }t d
t| j d|dd d S )Nu   Loading MMS LID-256 → z ()T)local_files_only)torch_dtyper   c                 s  s     | ]}|  |  V  qd S r   )numelelement_size).0pr   r   r   	<genexpr>/   s    zMMSLID.load.<locals>.<genexpr>    .AzMMS LID loaded: z languages, ~.0fzMB VRAM)loggerinfor   r   _ensure_localr   from_pretrainedr   r   toevalr   configid2labelr   sum
parameterslen)r   r   localvram_mbr   r   r   load'   s   $zMMSLID.loadreturnc                  C  s  t d  rtd tt S ddl} tdd}td}td| d	t	  | j
d
tdtdtddd}||t	t| | jd }td|dd t|d}|jddd W d   n1 smw   Y  |jdd tdt   tt S )z8Download MMS LID from R2 if not already present locally.zconfig.jsonzMMS LID already cached locallyr   NR2_VALIDATION_MODEL_BUCKETzvalidation-resultsz/tmp/mms-lid-256.tarz"Downloading MMS LID from R2: s3:///s3R2_ENDPOINT_URLR2_ACCESS_KEY_IDR2_SECRET_ACCESS_KEYauto)endpoint_urlaws_access_key_idaws_secret_access_keyregion_namer$   zDownloaded r%   zMB, extracting...zr:*z/tmpdata)filterT)
missing_okzMMS LID extracted to )
_LOCAL_DIRexistsr&   r'   r   boto3osgetenvr   _R2_KEYclientdownload_filestatst_sizetarfileopen
extractallunlink)rE   buckettar_pathr7   size_mbtfr   r   r   r(   2   s,   
zMMSLID._ensure_localc                 C  s"   | ` | `d  | _ | _tj  d S r   )r   r   torchr   empty_cacher   r   r   r   unloadQ   s   zMMSLID.unload>  	waveformslist[torch.Tensor]sample_rateint
list[dict]c                 C  sH   g }t dt|| jD ]}|||| j  }| ||}|| q|S )z
        Run LID on a list of mono waveforms (each [samples] float32).
        Returns list of dicts with keys:
          mms_lang_iso3, mms_lang_iso1, mms_confidence, mms_top3
        r   )ranger0   r   _infer_batchextend)r   rZ   r\   resultsi
batch_wavsbatch_resultsr   r   r   predict_batchV   s   	zMMSLID.predict_batchc                   s   dd |D } j ||ddd} fdd| D } jdi |j}tj| dd	}g }t|jd
 D ]<}|| }	|		d\ j
d
   }
d
  }t|
|
} fddtdD }||
|t|d|d q6|S )Nc                 S  s   g | ]}|  qS r   )numpy)r!   wr   r   r   
<listcomp>h   s    z'MMSLID._infer_batch.<locals>.<listcomp>ptT)sampling_ratereturn_tensorspaddingc                   s2   i | ]\}}||j  j|jjr jn|jd qS ))r   )r*   r   r   is_floating_point)r!   kvrW   r   r   
<dictcomp>m   s    &z'MMSLID._infer_batch.<locals>.<dictcomp>)dimr      c                   s2   g | ]} j |   t|  d dqS )   )langconf)r   itemround)r!   jr   top3_idx	top3_valsr   r   ri   |   s    ru   )mms_lang_iso3mms_lang_iso1mms_confidencemms_top3r   )r   itemsr   logitsFsoftmaxfloatr_   shapetopkr   rx   r
   getappendry   )r   rZ   r\   
raw_arraysinputsr   probsrb   b	row_probs	top1_iso3	top1_conf	top1_iso1top3r   r{   r   r`   f   s6   

zMMSLID._infer_batchN)r   r   r   r   )r   )r   r   )r4   r   )rY   )rZ   r[   r\   r]   r4   r^   )__name__
__module____qualname____doc__rU   float16r   r3   staticmethodr(   rX   inference_moderf   r`   r   r   r   r   r      s    r   )r   
__future__r   loggingrF   rM   pathlibr   typingr   rU   torch.nn.functionalnn
functionalr   transformersr   r   r,   r   r	   r
   	getLoggerr   r&   rC   rH   r   r   r   r   r   <module>   s    
