jĒ0SSKJr SSKJrJrJr SSKrSSKrSSK J r SSK J r SSK JrJrJrJr SS KJr SS KJr \(aSS KJr SS KJr SS KJr \R8"\5r\"5(a SSKrSSK J!r! Sr"Sr#SSjr$Sr%"SS\5r&g)) defaultdict) TYPE_CHECKINGAnyUnionN)GenerationConfig)PreTrainedTokenizer)is_torch_availableis_torchaudio_availableis_torchcodec_availablelogging) ffmpeg_read) ChunkPipeline)BeamSearchDecoderCTC)SequenceFeatureExtractor)PreTrainedModel)(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMESc6URS:XaU$URS:wa/[SURS[UR5S35eURSnUS:a%[SUS[UR5S 35eUR SS 9$) a Reduce a waveform to a single channel, or refuse when the layout is ambiguous. The pipeline reads audio as `(channels, samples)`, which is what torchcodec returns and what `datasets` returned before 4.0. Anything else is rejected rather than guessed at: the shape alone cannot distinguish two-channel audio from a two-sample recording, and averaging the wrong axis produces a silently wrong transcription instead of an error. rrzAutomaticSpeechRecognitionPipeline expects mono audio shaped `(samples,)` or multi-channel audio shaped `(channels, samples)`, got z dimensions with shape .rzMAutomaticSpeechRecognitionPipeline reads axis 0 as channels, and it has size z in the input of shape a. If this array is channels-last `(samples, channels)`, which is what `soundfile.read`, `librosa.load(mono=False)` and `scipy.io.wavfile.read` return, transpose it first. If it genuinely has more than 2 channels, downmix it to mono yourself so the weighting is yours to choose.axis)ndim ValueErrortupleshapemean)inputs num_channelss C:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\transformers/pipelines/automatic_speech_recognition.py_to_monor!)s{{a  {{a EEK[[MR -.a 1  <<?La [n3E&,,4G3HI3 3   ;;A; c/nUHcup4n[[X1-55n[[XC- U-55n[[XS- U-55nXdU4nURU5 Me U$)z Rescales the stride values from audio space to tokens/logits space. (160_000, 16_000, 16_000) -> (2000, 200, 200) for instance. )introundappend)strideratio new_stridesinput_nleftrighttoken_n new_strides r rescale_strider/KsuK &ueGO,-5'123E%/G345U+ :& !' r"c#X# URSnX#- U- n[SXg5HnX-n XU n U"U URSSS9n UbU RUS9n US:XaSOUn X:n U (aSOUnU RSnX,U4nU RSU :aXS.U Ev U (dM g g7f)NrptT sampling_ratereturn_tensorsreturn_attention_maskdtype)is_lastr')rranger3to)rfeature_extractor chunk_len stride_left stride_rightr7 inputs_lenstepchunk_start_idx chunk_end_idxchunk processed _stride_leftr8 _stride_rightr's r chunk_iterrG_saJ  "\ 1D J5'3 }5% +99"&   ! 5 1I+q0qk -$, KKN =9 ;;q>L (%E9E E 7 )6s BB*%B*c hUSSR5Vs/sHo"UR;dMUPM nnUSSHnUSR5Vs/sHo"UR;dMUPM nnSnSn[S[U5S-5HinUS- n [R "[R "X8*S5[R "USU5:H5n X- U -n U S:dM^X:dMeUnU nMk URXVS5 M [R "U5$s snfs snf)Nrrgg@)tolistall_special_idsr9lennpsumarrayextend) sequences tokenizertok_idsequencenew_seq new_sequenceindexmax_iepsmatchesmatchings r _find_longest_common_sequencer\ys#&/q\!_%;%;%=i%=6yOhOhAh%=HiQR=-4QZ->->-@l-@6R[RkRkDk-@ lq#l+a/0Ag+CffRXXhrsm4bqAQ8RRSG{S(H{x1  V,-! 88H jlsD*D*D/,D/c N^\rSrSrSrSrSrSrSrSr \ "SSS9r SS S S \ S \ 4S-S \S-S\ S\ 4S-S\ \S4S-4 U4SjjjrS\R$\-\ -\-S\S\\\ \44U4SjjrSSjr\S5rS SjrS!SjrS"S\S-4SjjrSrU=r$)#"AutomaticSpeechRecognitionPipelinea Pipeline that aims at extracting spoken text contained within some audio. The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for to support multiple audio formats Unless the model you're using explicitly sets these generation parameters in its configuration files (`generation_config.json`), the following default values will be used: - max_new_tokens: 256 - num_beams: 5 Example: ```python >>> from transformers import pipeline >>> transcriber = pipeline(model="openai/whisper-base") >>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac") {'text': ' He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered flour-fatten sauce.'} ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) Arguments: model ([`PreTrainedModel`]): The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from [`PreTrainedModel`]. feature_extractor ([`SequenceFeatureExtractor`], *optional*): The feature extractor that will be used by the pipeline to encode waveform for the model. tokenizer ([`PreTrainedTokenizer`], *optional*): The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from [`PreTrainedTokenizer`]. decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*): [PyCTCDecode's BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180) can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information. device (Union[`int`, `torch.device`], *optional*): Device ordinal for CPU/GPU supports. Setting this to `None` will leverage CPU, a positive will run the model on the associated CUDA device id. TF)max_new_tokens num_beamsNmodelrr;rrQdecoderrdevicez torch.devicec P>URRS:XaSUlOoURR[ R "5;aSUlO:URRS;aSUlOUbX@lSUlOSUl[TU]$"XU4SU0UD6 g) Nwhisperseq2seq_whisperseq2seq) parakeet_tdt parakeet_rnntnemotron_asr_streamingnemotron3_5_asrtdt ctc_with_lmctcrf) config model_typetype __class____name__rvaluesresuper__init__)selfrdr;rQrerfkwargsrus r ry+AutomaticSpeechRecognitionPipeline.__init__s << " "i /)DI __ % %)Q)X)X)Z Z!DI \\ $ $(v vDI  "L%DIDI +<VVVvVr"rr{returnc &>[TU]"U40UD6$)a Transcribe the audio sequence(s) given as inputs to text. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more information. Args: inputs (`np.ndarray` or `bytes` or `str` or `dict`): The inputs is either : - `str` that is either the filename of a local audio file, or a public URL address to download the audio file. The file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system. - `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the same way. - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`) Raw audio at the correct sampling rate (no further check will be done) - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "raw": np.array}` with optionally a `"stride": (left: int, right: int)` than can ask the pipeline to treat the first `left` samples and last `right` samples to be ignored in decoding (but used at inference to provide more context to the model). Only use `stride` with CTC models. return_timestamps (*optional*, `str` or `bool`): Only available for pure CTC models (Wav2Vec2, HuBERT, etc) and the Whisper model. Not available for other sequence-to-sequence models. For CTC models, timestamps can take one of two formats: - `"char"`: the pipeline will return timestamps along the text for every character in the text. For instance, if you get `[{"text": "h", "timestamp": (0.5, 0.6)}, {"text": "i", "timestamp": (0.7, 0.9)}]`, then it means the model predicts that the letter "h" was spoken after `0.5` and before `0.6` seconds. - `"word"`: the pipeline will return timestamps along the text for every word in the text. For instance, if you get `[{"text": "hi ", "timestamp": (0.5, 0.9)}, {"text": "there", "timestamp": (1.0, 1.5)}]`, then it means the model predicts that the word "hi" was spoken after `0.5` and before `0.9` seconds. For the Whisper model, timestamps can take one of two formats: - `"word"`: same as above for word-level CTC timestamps. Word-level timestamps are predicted through the *dynamic-time warping (DTW)* algorithm, an approximation to word-level timestamps by inspecting the cross-attention weights. - `True`: the pipeline will return timestamps along the text for *segments* of words in the text. For instance, if you get `[{"text": " Hi there!", "timestamp": (0.5, 1.5)}]`, then it means the model predicts that the segment "Hi there!" was spoken after `0.5` and before `1.5` seconds. Note that a segment of text refers to a sequence of one or more words, rather than individual words as with word-level timestamps. generate_kwargs (`dict`, *optional*): The dictionary of ad-hoc parametrization of `generate_config` to be used for the generation call. For a complete overview of generate, check the [following guide](https://huggingface.co/docs/transformers/en/main_classes/text_generation). Return: `Dict`: A dictionary with the following keys: - **text** (`str`): The recognized text. - **chunks** (*optional(, `list[Dict]`) When using `return_timestamps`, the `chunks` will become a list containing all the various text chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamp": (0.5, 0.9)}, {"text": "there", "timestamp": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing `"".join(chunk["text"] for chunk in output["chunks"])`. )rx__call__)rzrr{rus r r+AutomaticSpeechRecognitionPipeline.__call__srw1&11r"c 0n0n 0n UbGURS;a3U(d,Sn URS:XaU S- n [RU 5 XS'UbX(S'SU;a U RUR S55 U RU5 [ USS5bUR U S'[ US S5bURU S 'URU S 'UbXJS 'Ub#URS:wa [S 5eXjS 'XiS '[US5(a:[URS5(aU=(d URRnUbURS:XaU(a [S5eURS:XaUS:wa [S5eURS:XaUS;a [S5eURS:XaUS:Xa [S5eXYS'XZS'XU 4$)N)rjriaUsing `chunk_length_s` is very experimental with seq2seq models. The results will not necessarily be entirely accurate and will have caveats. More information: https://github.com/huggingface/transformers/pull/20104. Ignore this warning with pipeline(..., ignore_warning=True).riz To use Whisper for long-form transcription, use rather the model's `generate` method directly as the model relies on it's own chunking mechanism (cf. Whisper original paper, section 3.8. Long-form Transcription).chunk_length_sstride_length_sgenerate_kwargsassistant_modelassistant_tokenizerrQdecoder_kwargsz)Only Whisper can return language for now.return_languagegeneration_configreturn_timestampsrjzEWe cannot return_timestamps yet on non-CTC models apart from Whisper!rpwordzRCTC with LM can only predict word level timestamps, set `return_timestamps='word'`rq)charrzCTC can either predict character level timestamps, or word level timestamps. Set `return_timestamps='char'` or `return_timestamps='word'` as required.rzWhisper cannot return `char` timestamps, only word level or segment level timestamps. Use `return_timestamps='word'` or `return_timestamps=True` respectively.) rtloggerwarningupdatepopgetattrrrQrrhasattrrr) rzrrignore_warningrrrrpreprocess_paramsforward_paramspostprocess_params type_warnings r _sanitize_parameters7AutomaticSpeechRecognitionPipeline._sanitize_parameterss,  %yy::>- 99 11 4L |,2@. /  &3B/ 0  /  ! !/"5"56G"H Io. 4*D 1 =040D0DN, - 4. 5 A*...N; '484L4LN0 1  %3A/ 0  &yy-- !LMM4C0 10?, - 4, - -'$:P:PRe2f2f 1 ]T5K5K5]5]   (yyI%*; !hiiyyM).?6.I !uvvyyE!&7?O&O `yy--2Cv2M _3D. /6G2 3 2DDDr"c[URRSS5nURRRS:XaXRR -nU$)zSample stride per output.inputs_to_logits_ratiorlasr_ctc)rrdrrrsr; hop_length)rzalign_tos r _align_to,AutomaticSpeechRecognitionPipeline._align_tomsP 4::,,.FJ ::   ' ': 5 ..99 9Hr"c #. # [U[5(aqURS5(dURS5(a [R"USS9R nO%[ US5nUR5nSSS5 [U[5(a[XRR5nSn0n[5(a<SSK n[XR5(aUR5R!5n[#5(a|SSKn[XR&R(5(aTUR+5n U R,n U R.S:XaU R0SS:XaU SOU n XR2S .n[U[45(GaUR7S S5nS U;a S U;dS U;d [9S5eUR7S S5n U c$UR7SS5 UR7S S5n UR7S 5n UnU n[U[:R<WR45(a [?U5nXRR:wa[A5(aSSK!J"n O [GS5eU RI[U[:R<5(aURJ"U5OUU URR5R!5nURRU - nOSnUbiUSUS-UR0S:a [9S5eUR0S[M[OUSU-55[M[OUSU-554n[U[:R<WR45(d[QS[SU5S35e[?U5nU(GaUcUS- n[U[L[T45(aX3/nURVn[M[OX RR-U- 5U-5n[M[OUSURR-U- 5U-5n[M[OUSURR-U- 5U-5nUUU-:a [9S5e[YXRUUUURZ5H n0UEUEv M gURRS:XaPUR0SURR\:a)URUURRSSSSS9nO`URRS:Xa*Uc'URUURRSSS9nO&URUURRSSS9nURZbUR_URZS9nUb URRS:Xa [9S5eUUS 'S S0UEUEv g!,(df  GN=f7f)!Nzhttp://zhttps://T)follow_redirectsrbrrr)rNr3r'r3rawrNzWhen passing a dictionary to AutomaticSpeechRecognitionPipeline, the dict needs to contain a "raw" key containing the numpy array or torch tensor representing the audio and a "sampling_rate" key, containing the sampling_rate associated with that arraypath) functionalztorchaudio is required to resample audio samples in AutomaticSpeechRecognitionPipeline. 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