A system for generating a contextual meeting transcript, the system comprising: one or more processors; and a non-transitory computer-readable storage medium storing instructions, which when executed by the one or more processors cause the one or more processors to: generate a textual transcript of a recording of a meeting, wherein the textual transcript comprises a plurality of utterances for a plurality of users attending the meeting; match, using voice data of each of the plurality of users, each utterance of the plurality of utterances with a corresponding user of the plurality of users; select an utterance of the plurality of utterances; generate, from a corresponding portion of the recording associated with the utterance of the plurality of utterances, a plurality of voice characteristics for the utterance; generate an input vector for the utterance, the input vector comprising values generated based on the plurality of voice characteristics and a portion of the textual transcript associated with the utterance; select a tonal machine learning model associated with a user corresponding to the utterance; input the input vector into the tonal machine learning model to obtain a tone associated with the utterance, wherein the tonal machine learning model was trained with training data specific to the user to predict various tones specific to the user; and generate a contextual transcript comprising an entry for the utterance of the plurality of utterances, wherein the entry comprises corresponding textual data associated with the utterance and metadata that includes a corresponding tone associated with the utterance. one or more processors; and a non-transitory computer-readable storage medium storing instructions, which when executed by the one or more processors cause the one or more processors to: generate a textual transcript of a recording of a meeting, wherein the textual transcript comprises a plurality of utterances for a plurality of users attending the meeting; match, using voice data of each of the plurality of users, each utterance of the plurality of utterances with a corresponding user of the plurality of users; select an utterance of the plurality of utterances; generate, from a corresponding portion of the recording associated with the utterance of the plurality of utterances, a plurality of voice characteristics for the utterance; generate an input vector for the utterance, the input vector comprising values generated based on the plurality of voice characteristics and a portion of the textual transcript associated with the utterance; select a tonal machine learning model associated with a user corresponding to the utterance; input the input vector into the tonal machine learning model to obtain a tone associated with the utterance, wherein the tonal machine learning model was trained with training data specific to the user to predict various tones specific to the user; and generate a contextual transcript comprising an entry for the utterance of the plurality of utterances, wherein the entry comprises corresponding textual data associated with the utterance and metadata that includes a corresponding tone associated with the utterance. generate a textual transcript of a recording of a meeting, wherein the textual transcript comprises a plurality of utterances for a plurality of users attending the meeting; match, using voice data of each of the plurality of users, each utterance of the plurality of utterances with a corresponding user of the plurality of users; select an utterance of the plurality of utterances; generate, from a corresponding portion of the recording associated with the utterance of the plurality of utterances, a plurality of voice characteristics for the utterance; generate an input vector for the utterance, the input vector comprising values generated based on the plurality of voice characteristics and a portion of the textual transcript associated with the utterance; select a tonal machine learning model associated with a user corresponding to the utterance; input the input vector into the tonal machine learning model to obtain a tone associated with the utterance, wherein the tonal machine learning model was trained with training data specific to the user to predict various tones specific to the user; and generate a contextual transcript comprising an entry for the utterance of the plurality of utterances, wherein the entry comprises corresponding textual data associated with the utterance and metadata that includes a corresponding tone associated with the utterance.