o uyg2@sddlZddlZddlmZddlmZddlZddlZddl m Z ddl m m ZedegdZdifddZ d+ddZd,ddZ d-ddZd-ddZd-ddZd.ddZd/ddZd0d!d"Zd1d$d%Zd2d'd(Zd-d)d*ZdS)3N)BytesIO) pad_sequence soundfile)flacmp3m4aoggopuswavwmatrainc cs|D]u}d|vs J|d}zNt|}tt|D]>}|dkr-|j|df|vr-q|t|j||dkrAi|Vqt||j|dfD]\}}i|||dVqLqWqt yx} zt d || WYd} ~ qd} ~ wwdS)z Give url or local file, return file descriptor Inplace operation. Args: data(Iterable[str]): url or local file list Returns: Iterable[{src, stream}] src inferenceuttr ) tts_indextts_textzFailed to open {}, ex info {}N) pq read_table to_pandasrangelenlocupdatedict enumerate Exceptionloggingwarningformat) datamodeZtts_datasampleurldfiindextextexr(I/home/splend1dchan/Desktop/BreezyVoice-dev/cosyvoice/dataset/processor.pyparquet_openers*   r*( Mb@?c cs|D]c}tt|d\|d<|d<|d=|dd|dd} | |kr)q| |kr.qt|d|kr7qt|d|kr@qt|ddkrIq| dkrct|d| |krXqt|d| |krcq|Vqd S) aX Filter sample according to feature and label length Inplace operation. Args:: data: Iterable[{key, wav, label, sample_rate}] max_length: drop utterance which is greater than max_length(10ms) min_length: drop utterance which is less than min_length(10ms) token_max_length: drop utterance which is greater than token_max_length, especially when use char unit for english modeling token_min_length: drop utterance which is less than token_max_length min_output_input_ratio: minimal ration of token_length / feats_length(10ms) max_output_input_ratio: maximum ration of token_length / feats_length(10ms) Returns: Iterable[{key, wav, label, sample_rate}] audio_dataspeech sample_rater.d text_token speech_tokenrN) torchaudioloadrsizer) r max_length min_lengthZtoken_max_lengthZtoken_min_lengthZmin_output_input_ratioZmax_output_input_ratior r! num_framesr(r(r)filter9s,r<"V>ccs|D]F}d|vs Jd|vsJ|d}|d}||kr2||kr"q||d<tjj||d||d<|d}|dkrF|d|<|VqdS)z Resample data. Inplace operation. Args: data: Iterable[{key, wav, label, sample_rate}] resample_rate: target resample rate Returns: Iterable[{key, wav, label, sample_rate}] r2r1) orig_freqnew_freqr.N)r6 transformsResampleabsmax)rZ resample_rateZmin_sample_rater r!r2waveformmax_valr(r(r)resamplels(  rGccst|D]4}d|vs Jd|vsJd|vsJd|vsJ|d}||jdddd}||d<|d=|Vqd S) z Extract fbank Args: data: Iterable[{key, wav, label, sample_rate}] Returns: Iterable[{key, feat, label}] r2r1rr4rdimr. speech_featN)squeeze transpose)rfeat_extractorr r!rEmatr(r(r) compute_fbanks    rOccsv|D]5}tj|dtjd|d<tj|dtjd|d<|r5tj|ddd|d<tj|ddd|d<|VqdS)z Parse utt_embedding/spk_embedding Args: data: Iterable[{key, wav, label, sample_rate}] Returns: Iterable[{key, feat, label}] utt_embeddingdtype spk_embeddingrrHN)torchtensorfloat32F normalize)rrXr r!r(r(r)parse_embeddings rYccs\|}|D]%}d|vsJ|j|d|d|d<|dkr(|j|d|d|d<|VqdS)z Decode text to chars or BPE Inplace operation Args: data: Iterable[{key, wav, txt, sample_rate}] Returns: Iterable[{key, wav, txt, tokens, label, sample_rate}] r&)allowed_specialr4rrtts_text_tokenN)encode)r get_tokenizerrZr tokenizerr!r(r(r)tokenizes  r_'ccsbg}|D]}||t||kr!t||D]}|Vqg}qt||D]}|Vq)dS)z Local shuffle the data Args: data: Iterable[{key, feat, label}] shuffle_size: buffer size for shuffle Returns: Iterable[{key, feat, label}] N)appendrrandomshuffle)rZ shuffle_sizer bufr!xr(r(r)rcs     rcccsng}|D]}||t||kr$|jddd|D]}|Vqg}q|jddd|D]}|Vq/dS)a{ Sort the data by feature length. Sort is used after shuffle and before batch, so we can group utts with similar lengths into a batch, and `sort_size` should be less than `shuffle_size` Args: data: Iterable[{key, feat, label}] sort_size: buffer size for sort Returns: Iterable[{key, feat, label}] cS|ddSNrJrr8rer(r(r)zsort..)keycSrgrhrirjr(r(r)rkrlN)rarsort)r sort_sizer rdr!rer(r(r)rns  rnccsJg}|D]}||t||kr|Vg}qt|dkr#|VdSdS)z Static batch the data by `batch_size` Args: data: Iterable[{key, feat, label}] batch_size: batch size Returns: Iterable[List[{key, feat, label}]] rN)rar)r batch_sizerdr!r(r(r) static_batchs     rr.ccsg}d}|D]8}d|vsJt|dtjsJ|dd}t||}|t|d}||kr:|V|g}|}q||qt|dkrK|VdSdS)a Dynamic batch the data until the total frames in batch reach `max_frames_in_batch` Args: data: Iterable[{key, feat, label}] max_frames_in_batch: max_frames in one batch Returns: Iterable[List[{key, feat, label}]] rrJr.N) isinstancerTTensorr8rDrra)rmax_frames_in_batchr rdZlongest_framesr!Znew_sample_framesZframes_after_paddingr(r(r) dynamic_batch s"      rwstaticcCsJ|dkr t|dS|dkrt||S|dkrt||Std|dS)z& Wrapper for static/dynamic batch rr.rxdynamiczUnsupported batch type {}N)rrrwrfatalr)rZ batch_typerqrvr r(r(r)batch)s   r{c #s|D]tts JtjddDtjd}tj|dd}fdd|D}fdd|D}tjdd|Dtjd}t|dd d }fd d|D}tjd d|Dtjd}t|dd d }fd d|D} fdd|D} tjdd| Dtjd} t| dd d } tjfdd|Dd d} tjfdd|Dd d} |||||| | | | | d }|dkrfdd|D}fdd|D}fdd|D}tjdd|Dtjd}t|ddd }|||||d|dur|d|d<n|d|d<|VqdS)z Padding the data into training data Args: data: Iterable[List[{key, feat, label}]] Returns: Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)] cSsg|] }|ddqS)rJr.ri).0rer(r(r) Bszpadding..rQT) descendingcg|]}|dqS)rr(r|r$r!r(r)r}Fcg|] }t|dqS)r5rTrUrrr(r)r}GcSg|]}|dqSrrirr(r(r)r}Hr) batch_first padding_valuecr)rJr(rrr(r)r}LrcSrrrirr(r(r)r}Mrcr)r&r(rrr(r)r}Qrcr)r4rrrr(r)r}RrcSrrrirr(r(r)r}Srcr)rPr(rrr(r)r}UrrHcr)rSr(rrr(r)r}Vr) uttsr5speech_token_lenrJspeech_feat_lenr&r4text_token_lenrPrSrcr)rr(rrr(r)r}drcr)rr(rrr(r)r}ercr)r[rrrr(r)r}frcSrrrirr(r(r)r}gr)rrr[tts_text_token_lenrS embeddingrPN) rtlistrTrUint32argsortrstackr)rZuse_spk_embeddingr rorderrr5rrJr&r4rrPrSr{rrr[rr(rr)padding7sj   r)r+r,r-r.r/r.r )r=r>r )r )r`r )rfr )rp)rsr )rxrprsr )rrbpyarrow.parquetparquetriorrTr6torch.nn.utils.rnnrZtorch.nn.functionalnn functionalrWset_audio_backendsetZAUDIO_FORMAT_SETSr*r<rGrOrYr_rcrnrrrwr{rr(r(r(r)s<       3