Njl bddlZddlZddlZddlZddlZddlZddlZddlZddlm Z ddl m Z ddl Z ddl Z ddlZddlZddlmZddlmZddlmZddlmZddlmZdd lmZdd lmZmZm Z m!Z!m"Z"m#Z#dd l$m%Z%dd l&m'Z'dd l(m)Z)ddl*m+Z+ddl,m-Z-m.Z.ddl/m0Z0m1Z1m2Z2ddl3m4Z4m5Z5ddl6m7Z7m8Z8m9Z9ddl:m;Z;mZ>m?Z?m@Z@ddlAmBZBmCZCejdejeFZGdeHe;jdZJGddejZLdeMdeNdeNdeOeMePffdZQdeHePd eHePdeHePfd!ZRdeHePd"eHePdeHeOePeMffd#ZSd$eMd%ePdeNdeNdeHeMf d&ZTd'eUeMeUeMejffd(eMe zfd)ZWd*eMe zd+ejdeUeMeUeMejfffd,ZYy)-N) lru_cache)Path)nn) functional)Self) TokenizedText) audio_read) convert_audio)DEFAULT_EOS_THRESHOLDDEFAULT_LANGUAGEDEFAULT_LSD_DECODE_STEPSDEFAULT_NOISE_CLAMPDEFAULT_TEMPERATUREMAX_TOKEN_PER_CHUNK) FlowLMModel) MimiModel)mimi_transformer)DummyQuantizer) SEANetDecoder SEANetEncoder)StatefulModuleincrement_steps init_states)RECOMMENDED_CONFIGapply_dynamic_int8) CONFIGS_DIRConfig load_config)_ORIGINS_OF_PREDEFINED_VOICES DEBUG_MIMIdisplay_execution_timedownload_if_necessaryget_predefined_voice size_of_dict)get_flow_lm_state_dictget_mimi_state_dictzWe could not download the weights for the model with voice cloning, but you're trying to use voice cloning. 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If you want access to the model with voice cloning, go to https://huggingface.co/kyutai/pocket-tts and accept the terms, then make sure you're logged in locally with `uvx hf auth login`.ceZdZdZdZ d>dededededzd ed e dzd e d edzd e ffd Z e de jfdZe defdZed ededzd e dzdefdZe d?d ededzd e dzdefdZeddeeeedfdedzd ee zdzdeezdedeezdzdede defdZ d@dede j6dzde j6dzde j6dzdee j6e j6ff dZdede j6de j6de j6dee j6e j6ff dZde j6defd Zd!e j6de j6fd"Z ded#eddfd$Z!dedefd%Z"e jFd&e$jJd'e$jJd(ed)efd*Z&e jFe'dd+fded,ed-ed.edzd/e de j6f d0Z(e jFe'dd+fded,ed-ed.edzd/e f d1Z)e jFded,ed.ed/e fd2Z*e jFded3e+d4ed.ed&e$jJd'e$jJf d5Z,e jFded4ed.ed&e$jJfd6Z-e.d78 dAde eze j6zd9e defd:Z/e jF dAde eze j6zd9e defd;Z0d)r.mimi sample_raterBs r:rEzTTSModel.sample_rate`s{{+++r;c tj|j|jjj |jj }|||||||||j|j|j }|S)N) latent_diminsert_bos_before_voice)r/r0r1r2) rfrom_pydantic_configr*rD quantizer dimensionrHr0r1r2) clsr.r+r,r-r6r/r* tts_models r:_from_pydantic_configzTTSModel._from_pydantic_configds22 NN{{,,66$*NN$J$J        -3-T-T/5/X/X$66  r;c ^ |j||||||}tjjtj|j j j|jjxs |jjjftj|j _ |j j|jj tdt j#d|j jt%t'|j j}|j j)|d|jj+} t-di| d} t/di| d} t1j2di| d} t1j2di| d} t5di| d }t7| | || d | d | d | d | j8z | d | d| |  j;d|_|jj|j j tdt j#d|jjt=t'|jj}|jj)|d|jj?|jjt j#d|j t'|j}tFjjI|}|j)|d|j j!|jt jKdtM|jOdz}tPjRjUdddk(rHd}tFjjW|jO|t j#d|tYj"d|d|j |jfD]2}|j[D]\}}t]|t^s||_04|S#t@$r d|_!t'|jD}YwxYw) Nr/)dtypezHIf you specify flow_lm.weights_path you should specify mimi.weights_pathzLoading FlowLM weights from T)strictseanet transformerrJchannelsrE frame_rate inner_dim outer_dim)rUrErVencoder_frame_raterWrXencoder_transformerdecoder_transformercpu)rAzHIf you specify mimi.weights_path you should specify flow_lm.weights_pathzLoading Mimi weights from zLoading TTSModel weights from FzINo weights_path specified for FlowLM or TTSModel, model is uninitialized!.APOCKET_TTS_SAVE_WEIGHTS01z./model.safetensorszSaved TTSModel weights to z+TTS Model loaded successfully. Its size is z MB)1rNtorchr Parameterzerosr*rTd_modelrDrWrSrKfloat32speaker_proj_weight weights_path ValueErrorloggerinfor%r"load_state_dict model_dumprrrProjectedTransformerrr hop_lengthtor&eval Exceptionr7"weights_path_without_voice_cloning safetensors load_filewarningr$ state_dictosenvironget save_filelogging named_modules isinstancer_module_absolute_name)rLr.r+r,r-r6r/rMstate_dict_flowlm mimi_configencoderdecoderrZr[rJ mimi_state weights_filerw size_in_mb save_path top_module module_namemodules r:"_from_pydantic_config_with_weightsz+TTSModel._from_pydantic_config_with_weightss*-- D*Kv. 160B0B KKNN..66KK))IV[[-?-?-I-Imm  1 - >> & & 2{{''/ ^ KK6v~~7R7R6ST U 6%fnn&A&AB!     - -.? - Mkk,,.  8+h"788+h"78.CCakR_F`a.CCakR_F`a">[%=> "    ,#M2"<0*=9G >> & & .63F3F3N NN[ ")"6"6"89S@ ::>>3S 9S @-I    ' ' (<(<(> J KK4YK@ A B:,cRS%,,inn=J'1'?'?'A# V!&.9/:,(B> 3 `.3 +4V5^5^_  `sR%R,+R,languager6quantizec| | td||t}||dk(r tdt|dz }t|}|jdvr tdt|}t |}t jd|dtj|||||| } |rt| jt| S) a Load a pre-trained TTS model with specified configuration. This class method loads a complete TTS model including the flow language model and Mimi compression model from pre-trained weights. The model is initialized with the specified generation parameters and ready for inference. Args: language: Optional language identifier to select a predefined config. Incompatible with the `config` argument. Available options are `"english_2026-01"`, `"english_2026-04"`, `"english"`, `"french_24l"`, `"german_24l"`, `"portuguese"`, `"italian"`, `"spanish_24l"`. If neither `config` nor `language` is provided, defaults to `"english", which is the same model as 'english_2026-04'`. config: A path to a custom YAML config file saved locally (e.g., `"C://pocket_tts/pocket_tts_config.yaml"`). temp: Sampling temperature for generation. Higher values produce more diverse but potentially lower quality output. lsd_decode_steps: Number of steps for Lagrangian Self Distillation decoding. More steps can improve quality but increase computation. noise_clamp: Maximum value for noise sampling. If None, no clamping is applied. Helps prevent extreme values in generation. eos_threshold: Threshold for end-of-sequence detection. Higher values make the model more likely to continue generating. quantize: If True, apply dynamic int8 quantization to the transformer's attention and FFN layers. Reduces runtime memory by ~48% and improves inference speed by ~27% on x86 (FBGEMM). No measurable impact on speech quality (WER unchanged). For optimized performance, install torchao: ``pip install pocket-tts[quantize]`` Returns: TTSModel: Fully initialized model with loaded weights on cpu, ready for text-to-speech generation. Raises: FileNotFoundError: If the specified config file or model weights are not found. ValueError: If the configuration is invalid or incompatible. Example: ```python from pocket_tts import TTSModel # Load with default settings model = TTSModel.load_model() # Load with int8 quantization model = TTSModel.load_model(quantize=True) ``` zHCannot specify both config and language, please choose one or the other.frenchzzFor technical reasons, only a larger 24-layer model is available for French. Please use the 'french_24l' language instead..yaml)rz.ymlz8Config should be a path to a YAML file ending with .yamlzLoading model from config at z...rP) rir rrsuffixrrjrkr)rrr*r) rLrr.r+r,r-r6r config_pathrMs r: load_modelzTTSModel.load_modelsr  ("6Z  >h.'H  8# Q!hZu#55Ff == 1 1WX X6l [) 3K=DE?? D*K{@   y002D Er; model_state text_tokensbackbone_input_latentsaudio_conditioningc|:tjdtj|jj}|Wtj dd|jj f|jj|jj}|Wtj dd|jjf|jj|jj}|j||||}|jd|jdz|jdz}t|j|||S)zJFirst one is the backbone output, second one is the audio decoding output.)r'r)rQrAr'r)rrrr increment) rbrdint64r*rAemptyldimrQdim _run_flow_lmshaper)r8rrrroutput increment_bys r:_run_flow_lm_and_increment_stepz(TTSModel._run_flow_lm_and_increment_step=s   ++fEKK H[H[\K ! )%*[[At||(()1C1CDLLL_L_& "  %!&At||''( 0B0B4<?,:L)+*=r;c|jD]B}|jd}|t|jddj cSt d)NoffsetrrznCould not find offset in model state, please open an issue at https://github.com/kyutai-labs/pocket-tts/issues)valuesrzintviewitemri)r8rrrs r:_flow_lm_current_endzTTSModel._flow_lm_current_endsa'..0L!%%h/F!6;;r?1-224551 B  r; latents_queue result_queuemimi_sequence_lengthmimi_steps_per_latentcv g}t|jd|} |j}|nO||jjz|jj z}|j dd} |jj| } tj} |jj| |} t|j||| jd|jjjz } tj!dt#| d zt#tj| z d z|j%| |j'd | f|j)c|j'd y#t*$r}|j'd |fYd}~yd}~wwxYw) zVWorker thread function for decoding audio latents from queue with immediate streaming.r'rNrrrrzG Decoded %d ms of audio with mimi in %d mschunk)doneNerror)rrDrzr*emb_stdemb_meanrrJtime monotonicrrrr.rErjdebugrappendput task_donerr)r8rrrr audio_chunksrlatentmimi_decoding_input transposed quantizedt audio_frameaudio_frame_durationes r:_decode_audio_workerzTTSModel._decode_audio_workersz +L$TYY1NbcJ&**,>&,t||/C/C&CdllF[F[&[#0::2rB  II// ; NN$"ii::9jQ  :AVW'2'8'8';dkk>N>N>Z>Z'Z$ J,t34)A-56 ##K0  ';!78'')-2   ^ , +   gq\ * * +sFF F8F33F8Ttext_to_generate max_tokensframes_after_eos copy_statecg}|j|||||D]}|j|tj|dS)aGenerate complete audio tensor from text input. This method generates the full audio output for the given text prompt and returns it as a single tensor. It internally uses the streaming generation method but collects all chunks before returning. This method is NOT thread-safe; separate model instances should be used for concurrent generation. Args: model_state: Model state dictionary containing hidden states and positional information. Can be obtained from get_state_for_audio_prompt() or init_states(). The state may be modified during generation. text_to_generate: Input text to convert to speech. The text will be automatically formatted (capitalization, punctuation) for optimal generation quality. frames_after_eos: Number of additional frames to generate after detecting end-of-sequence. If None, automatically determined based on text length (1-3 frames). copy_state: Whether to create a deep copy of the model state before generation. If True, preserves the original state for reuse. If False, modifies the input state in-place. Defaults to True. Returns: torch.Tensor: Generated audio tensor with shape [channels, samples] at the model's sample rate (typically 24kHz). The audio is normalized and ready for playback or saving. You can get the sample rate from the `sample_rate` attribute. Raises: ValueError: If text_to_generate is empty or invalid. RuntimeError: If generation fails due to model errors. Example: ```python from pocket_tts import TTSModel model = TTSModel.load_model() voice_state = model.get_state_for_audio_prompt("hf://kyutai/tts-voices/alba-mackenna/casual.wav") # Generate audio audio = model.generate_audio(voice_state, "Hello world!", frames_after_eos=2, copy_state=True) print(f"Generated audio shape: {audio.shape}") print(f"Audio duration: {audio.shape[-1] / model.sample_rate:.2f} seconds") ``` )rrrrrrr)generate_audio_streamrrbr)r8rrrrrrrs r:generate_audiozTTSModel.generate_audiosVr //#--!! 0 E    & yy1--r;c#VK| |j}t|jjj|||j |j }|D]N}t||j |j \}}|dz }||n|} |j||| |Ed{Py7w)a Generate audio streaming chunks from text input. This method generates audio from text and yields chunks as they become available, enabling real-time playback or processing. It uses multithreading to parallelize generation and decoding for optimal performance. This method is NOT thread-safe; separate model instances should be used for concurrent generation. Args: model_state: Model state dictionary containing hidden states and positional information. Can be obtained from get_state_for_audio_prompt() or init_states(). The state may be modified during generation. text_to_generate: Input text to convert to speech. The text will be automatically formatted (capitalization, punctuation) for optimal generation quality. frames_after_eos: Number of additional frames to generate after detecting end-of-sequence. If None, automatically determined based on text length (1-3 frames). Defaults to None. copy_state: Whether to create a deep copy of the model state before generation. If True, preserves the original state for reuse. If False, modifies the input state in-place. Defaults to True. Yields: torch.Tensor: Audio chunks with shape [samples] at the model's sample rate (typically 24kHz). Chunks are yielded as soon as they are decoded, enabling real-time streaming. Raises: ValueError: If text_to_generate is empty or invalid. RuntimeError: If generation fails due to model errors or threading issues. Example: ```python from pocket_tts import TTSModel model = TTSModel.load_model() voice_state = model.get_state_for_audio_prompt("hf://kyutai/tts-voices/alba-mackenna/casual.wav") # Stream generation for chunk in model.generate_audio_stream(voice_state, "Long text content..."): # Process each chunk as it's generated print(f"Generated chunk: {chunk.shape[0]} samples") # Could save chunks to file or play in real-time ``` Note: This method uses multithreading to parallelize latent generation and audio decoding. Generation performance is logged including real-time factor (RTF) metrics. N)r2r)rrrr) r1split_into_best_sentencesr*r tokenizerr0r2prepare_text_prompt!_generate_audio_stream_short_text) r8rrrrrchunksrframes_after_eos_guesseffective_framess r:rzTTSModel.generate_audio_stream sv  ##FF  + LL $ $ . .    1 1"44  E7Jt<T>T8 4 4 #a ' "$4$@ F\ =='!&!1% >   sBB)B' B)c#K|rtj|}|jjj |}|j j d}|j|}t|jj|jjz }||z} tj} tj} tj|j | | | |fd} t"j%dt'j(} | j+|j-||||| | d} | j/}|ddk(r|d}||j dz }|d n:|dd k(rn2|dd k(r)t1d 5| j3ddd|drt1d 5| j3dddt|d z|j4jj6z }tt'j(| z d z}||z }t"j%d|||y#1swY|dxYw#1swYxYww)Nr'T)targetargsdaemonzstarting timer now!)rprepared max_gen_lenrrrrrr)rrrrz"Waiting for mimi decoder to finishrzAGenerated: %d ms of audio in %d ms so %.2fx faster than real-time)copydeepcopyr*rpreparetokensr_estimate_max_gen_lenrrDrYrVqueueQueue threadingThreadrrjrkrrstart _generaterzr!joinr.rE)r8rrrrr token_countr rrrrdecoder_thread t_generatingtotal_generated_samplesresult audio_chunkduration_generated_audiogeneration_timereal_time_factors r:rz*TTSModel._generate_audio_stream_short_textysO -- 4K<<++334DEoo++A. 00= #DII$@$@499CWCW$W X*-BB {{} #)),,/CEZ[   )*~~'  ##-'%  #$!%%'FayG#$Qi ';+<+$FG3oE O $    !RQiJ Is7FI'I )I'IA8I' I I'I$ I'rr cv|jjd}j}||zz} j| t d5j |jdddfd} t j| d} | jy#1swY;xYw)Nr')rzPrompting text)rrc jy#t$rO}tjd|j dj d|fYd}~yYd}~yd}~wwxYw)Nz$Error in autoregressive generation: r)_autoregressive_generationrrrjrr)rrrr rrr8s r:run_generationz*TTSModel._generate..run_generationsz 3//.>  3 CA3GH ,!%%d++ $$gq\22,  3s A0AA++A0T)rr) r rrrr!rrrr) r8rrr rrrr current_end required_lenr"generation_threads `` ```` r:rzTTSModel._generatesoo++A. // < "[0;>  k<H #$4 5  0 0'X__ 1 6 3 3&,,N4P!)6 5s B//B8c Xtjdd|jjft dt t |jjj|jj}g}d}t|D]}tdd5} |j||\} } | jr||}||||zk\r dddn{|j| | }ddd|j j t"j$j'dd d k(r t)d t*j-d |jdt*j/d t1t3j4|y#1swYxYw)Nr'r) fill_valuerArQzGenerating latentF) print_output)rrKPOCKET_TTS_ERROR_WITHOUT_EOSr_r`z.Generation reached maximum length without EOS!zRMaximum generation length reached without EOS, this very often indicates an error.z#Average generation step time: %d ms)rbrr*rrr?iterr@rArQranger!rrrrelapsed_time_msrxryrz RuntimeErrorrjrvrkr statisticsmean) r8rr rrbackbone_input steps_timeseos_stepgeneration_steptimer next_latentrs r:r!z#TTSModel._autoregressive_generationst 4<<$$ %U|T\\44678??,,$$    $[1O'(;%PTY&*&J&J +N'K'# V;;=X%5.H'OxJZ?Z,ZQP!!+.!,Q   u44 5 2zz~~=sCsJ"#STT NNd  $ 93z{?[;\]-QPs!6F !F  F) r)maxsizetruncatec&|j||Sr>)get_state_for_audio_prompt)r8rr7s r:"_cached_get_state_for_audio_promptz+TTSModel._cached_get_state_for_audio_prompt s../A8LLr;ct|ttfrKt|jdr1t|tr t |}t ||j St|tr|tvr|j|jjtstd|jt t t|jj||j S|js%t|ttfrttt|tr t |}t|tr~t!|\}}|rBt#d|z}|j$d|kDr"|dd|f}t&j)d|d t+|||j,j.j0d }t3d 5|j5|j7d j9|j }ddd|j:j<r-t?j@|j:jBgd }tE|j:d j$d }t3d5|jG||dddt&j)dtI|dz|S#1swYxYw#1swY9xYw)a Create model state conditioned on audio prompt for continuation. This method processes an audio prompt and creates a model state that captures the acoustic characteristics (speaker voice, style, prosody) for use in subsequent text-to-speech generation. The resulting state enables voice cloning and audio continuation with speaker consistency. Args: audio_conditioning: Audio prompt to condition (or .safetensors to load). Can be: - Path: Local file path to audio file (or .safetensors) - str: URL to download audio file (or .safetensors) from - torch.Tensor: Pre-loaded audio tensor with shape [channels, samples] truncate: Whether to truncate long audio prompts to 30 seconds. Helps prevent memory issues with very long inputs. Defaults to False. Returns: dict: Model state dictionary containing hidden states and positional information conditioned on the audio prompt. This state can be passed to `generate_audio()` or `generate_audio_stream()` for voice-consistent generation. Raises: FileNotFoundError: If audio file path doesn't exist. ValueError: If audio tensor is invalid or empty. RuntimeError: If audio processing or encoding fails. Example: ```python from pocket_tts import TTSModel model = TTSModel.load_model() # From HuggingFace URL voice_state = model.get_state_for_audio_prompt("hf://kyutai/tts-voices/alba-mackenna/casual.wav") # From local file voice_state = model.get_state_for_audio_prompt("./my_voice.wav") # Reload state from a .safetensors file (much faster than extracting from an audio file) voice_state = model.get_state_for_audio_prompt("./my_voices.safetensors") # From HTTP URL voice_state = model.get_state_for_audio_prompt( "https://huggingface.co/kyutai/tts-voices/resolve" "/main/expresso/ex01-ex02_default_001_channel1_168s.wav" ) ``` Note: - Audio is automatically resampled to the model's sample rate (24kHz) - The audio is encoded using the Mimi compression model and projected to the flow model's latent space - Processing time is logged for performance monitoring - The state preserves speaker characteristics for voice cloning z .safetensorsNzvCannot use predefined voices when the model is not loaded from a config associated with a language.Here the origin is )rnamer.z%Audio truncated to first 30 seconds (z samples)r'zEncoding audio promptrrrzPrompting audio)rrz/Size of the model state for audio prompt: %d MBr])%r~strrendswithr"_import_model_staterArr/is_relative_torrir#stemr7VOICE_CLONING_UNSUPPORTEDr rrrjrkr r.rDrEr!r unsqueezerpr*rHrbrbos_before_voicerrr$)r8rr7rconditioning_sample_rate max_samplespromptrs r:r9z#TTSModel.get_state_for_audio_promptst (3+ 63?Q;R;[;[ < ,c2%:;M%N"&'94;;G G )3 /"&CC{{"$++*D*D[*Q **.++8 '%($++2B2BI[\  %%*5G#t*U67 7 (# .!67I!J  ($ /.89K.L +E+!"'?"?@ ;;r?[0!#| |"34EKK"G }T] ^_!./1A1A1M1Mq" $$; <''(:(D(DQ(G(J(J4;;(WXF= << / /YY = =vFANF!$,,1fll[\o^ #$5 6  0 0[]c 0 d7  =|K?X\_?_ = <7 6s :KK!K!K*rc||jz |jz}|jjj}t j ||zSr>)_TOKENS_PER_SECOND_ESTIMATE_GEN_SECONDS_PADDINGr.rDrVmathceil)r8r gen_len_secrVs r:rzTTSModel._estimate_max_gen_lensF!D$D$DDtG`G`` [[%%00 yyz122r;)NFNFr>)NNN)F)3__name__ __module__ __qualname__rJrKrrrrrboolr5propertyrbrArE classmethodrrNrrr rr r>rdictTensortuplerrrrrrno_gradrrrrrrrrrr!rr:r9r __classcell__)r9s@r:r)r)>s"%#169="'333 3 T\ 33t 3+/3-0$J3 34. ..,S,, T\ t  8#dd T\ dt d ddL $$(/ 8*=4R*Rd T!Rck R  R S[4' RRR RRn,06:26 \\D(!& t 3  "LL4/  u||U\\) * >55\\5!& 5 "LL 5 u||U\\) * 5*] ]] 5<< ELL ;D;3;4;B    ]](+{{(+kk(+" (+ # (+(+T ]] .'+ A.A.A. 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