feat: add Nemo Conformer TDT support (pipeline-aligned refresh)

packages/transformers/src/models/feature_extractors.js

@@ -5,6 +5,7 @@ export * from './clap/feature_extraction_clap.js';
 export * from './dac/feature_extraction_dac.js';
 export * from './gemma3n/feature_extraction_gemma3n.js';
 export * from './moonshine/feature_extraction_moonshine.js';
+export * from './nemo_conformer_tdt/feature_extraction_nemo_conformer_tdt.js';
 export * from './parakeet/feature_extraction_parakeet.js';
 export * from './pyannote/feature_extraction_pyannote.js';
 export * from './seamless_m4t/feature_extraction_seamless_m4t.js';

packages/transformers/src/models/modeling_utils.js

@@ -118,6 +118,7 @@ export const MODEL_TYPES = {
     ImageAudioTextToText: 13,
     Supertonic: 14,
     Chatterbox: 15,
+    NemoConformerTDT: 16,
 };
 
 const MODEL_TYPE_CONFIG = {
@@ -876,8 +877,9 @@ export class PreTrainedModel extends Callable {
         if (input_name in model_inputs) {
             if (inputs) {
                 throw new Error(
-                    '`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. ' +
-                        'Make sure to either pass {inputs} or {input_name}=...',
+                    '`inputs` was passed alongside ' +
+                        `\`${input_name}\` which is not allowed. ` +
+                        `Make sure to either pass \`inputs\` or \`${input_name}\`=...`,
                 );
             }
         } else {

packages/transformers/src/models/models.js

@@ -103,6 +103,7 @@ export * from './mt5/modeling_mt5.js';
 export * from './multi_modality/modeling_multi_modality.js';
 export * from './musicgen/modeling_musicgen.js';
 export * from './nanochat/modeling_nanochat.js';
+export * from './nemo_conformer_tdt/modeling_nemo_conformer_tdt.js';
 export * from './neobert/modeling_neobert.js';
 export * from './nomic_bert/modeling_nomic_bert.js';
 export * from './olmo/modeling_olmo.js';

packages/transformers/src/models/nemo_conformer_tdt/feature_extraction_nemo_conformer_tdt.js

@@ -0,0 +1,240 @@
+import { FeatureExtractor, validate_audio_inputs } from '../../feature_extraction_utils.js';
+import { Tensor } from '../../utils/tensor.js';
+import { mel_filter_bank, spectrogram, window_function } from '../../utils/audio.js';
+import { logger } from '../../utils/logger.js';
+import { FeatureLRUCache, createAudioCacheKey } from './transducer_cache.js';
+import { computeTemporalDeltas } from './transducer_deltas.js';
+
+const EPSILON = 1e-5;
+
+/**
+ * Feature extractor for Nemo Conformer TDT models.
+ *
+ * Mirrors NeMo-style log-mel extraction used by Parakeet with configurable
+ * `feature_size` (e.g. 80 or 128 mel bins via `preprocessor_config.json`).
+ */
+export class NemoConformerTDTFeatureExtractor extends FeatureExtractor {
+    constructor(config) {
+        super(config);
+
+        if (!Number.isInteger(this.config.n_fft) || this.config.n_fft <= 0) {
+            throw new Error(
+                `NemoConformerTDTFeatureExtractor expected \`n_fft\` as a positive integer, got ${this.config.n_fft}.`,
+            );
+        }
+        if (
+            !Number.isInteger(this.config.win_length) ||
+            this.config.win_length <= 0 ||
+            this.config.win_length > this.config.n_fft
+        ) {
+            throw new Error(
+                `NemoConformerTDTFeatureExtractor expected \`win_length\` in [1, n_fft], got win_length=${this.config.win_length}, n_fft=${this.config.n_fft}.`,
+            );
+        }
+
+        // Prefer given `mel_filters` from preprocessor_config.json, or calculate them if they don't exist.
+        this.config.mel_filters ??= mel_filter_bank(
+            Math.floor(1 + this.config.n_fft / 2), // num_frequency_bins
+            this.config.feature_size, // num_mel_filters
+            0.0, // min_frequency
+            this.config.sampling_rate / 2, // max_frequency
+            this.config.sampling_rate, // sampling_rate
+            'slaney', // norm
+            'slaney', // mel_scale
+        );
+
+        const window = window_function(this.config.win_length, 'hann', {
+            periodic: false,
+        });
+
+        this.window = new Float64Array(this.config.n_fft);
+        const offset = Math.floor((this.config.n_fft - this.config.win_length) / 2);
+        this.window.set(window, offset);
+
+        // Optional feature-level cache and delta/delta-delta post-processing.
+        this.use_feature_cache = this.config.use_feature_cache ?? false;
+        this.delta_order = this.config.delta_order ?? 0;
+        this.delta_window = this.config.delta_window ?? 2;
+        this.delta_concatenate = this.config.delta_concatenate ?? true;
+
+        if (![0, 1, 2].includes(this.delta_order)) {
+            throw new Error(
+                `NemoConformerTDTFeatureExtractor expected delta_order in {0,1,2}, got ${this.delta_order}.`,
+            );
+        }
+        if (!Number.isInteger(this.delta_window) || this.delta_window < 1) {
+            throw new Error(
+                `NemoConformerTDTFeatureExtractor expected \`delta_window\` as a positive integer, got ${this.delta_window}.`,
+            );
+        }
+        if (this.delta_order > 0 && !this.delta_concatenate) {
+            logger.warn(
+                'NemoConformerTDTFeatureExtractor: `delta_concatenate=false` is set. ' +
+                    '`input_features` will remain base features and deltas are returned in extra fields.',
+            );
+        }
+
+        this.feature_cache = this.use_feature_cache
+            ? new FeatureLRUCache({
+                  max_entries: this.config.feature_cache_max_entries ?? 128,
+                  max_size_mb: this.config.feature_cache_max_size_mb ?? 64,
+              })
+            : null;
+    }
+
+    /**
+     * Computes the log-Mel spectrogram of the provided audio waveform.
+     * @param {Float32Array|Float64Array} waveform The audio waveform to process.
+     * @returns {Promise<Tensor>} An object containing the log-Mel spectrogram data as a Float32Array and its dimensions as an array of numbers.
+     */
+    async _extract_fbank_features(waveform) {
+        // Parakeet uses a custom preemphasis strategy: Apply preemphasis to entire waveform at once
+        const preemphasis = this.config.preemphasis ?? 0;
+        if (!Number.isFinite(preemphasis) || preemphasis < 0 || preemphasis >= 1) {
+            throw new Error(
+                `NemoConformerTDTFeatureExtractor expected \`preemphasis\` in [0, 1), got ${this.config.preemphasis}.`,
+            );
+        }
+        waveform = new Float64Array(waveform); // Clone to avoid destructive changes
+        if (preemphasis !== 0) {
+            for (let j = waveform.length - 1; j >= 1; --j) {
+                waveform[j] -= preemphasis * waveform[j - 1];
+            }
+        }
+
+        const features = await spectrogram(
+            waveform,
+            this.window, // window
+            this.window.length, // frame_length
+            this.config.hop_length, // hop_length
+            {
+                fft_length: this.config.n_fft,
+                power: 2.0,
+                mel_filters: this.config.mel_filters,
+                log_mel: 'log',
+                mel_floor: -Infinity,
+                pad_mode: 'constant',
+                center: true,
+
+                // Custom
+                transpose: true,
+                mel_offset: 2 ** -24,
+            },
+        );
+
+        return features;
+    }
+
+    /**
+     * Asynchronously extracts features from a given audio using the provided configuration.
+     * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array.
+     * @returns {Promise<{
+     *  input_features: Tensor;
+     *  attention_mask: Tensor;
+     *  delta_features?: Tensor;
+     *  delta_delta_features?: Tensor;
+     * }>} A Promise resolving to an object containing extracted model inputs.
+     *      When cache is enabled, tensor instances are shared and owned by the cache.
+     *      Do not mutate or dispose returned tensors unless cache is disabled/cleared.
+     */
+    async _call(audio) {
+        validate_audio_inputs(audio, 'NemoConformerTDTFeatureExtractor');
+
+        if (this.feature_cache) {
+            const key = `${createAudioCacheKey(audio, this.config.sampling_rate)}:${this.delta_order}:${this.delta_window}:${this.delta_concatenate}`;
+            const cached = this.feature_cache.get(key);
+            if (cached) {
+                return { ...cached };
+            }
+
+            const extracted = await this._extract(audio);
+            this.feature_cache.set(key, extracted);
+            return { ...extracted };
+        }
+
+        return await this._extract(audio);
+    }
+
+    async _extract(audio) {
+        const features = await this._extract_fbank_features(audio);
+
+        const [num_frames, num_features] = features.dims;
+        const raw_features_length = Math.floor(
+            (audio.length + Math.floor(this.config.n_fft / 2) * 2 - this.config.n_fft) / this.config.hop_length,
+        );
+        // Clamp to [0, num_frames] to avoid a negative fill offset for very short clips.
+        const features_length = Math.max(0, Math.min(num_frames, raw_features_length));
+
+        const features_data = /** @type {Float32Array} */ (features.data);
+        features_data.fill(0, features_length * num_features);
+
+        // normalize mel features, ignoring padding
+        const sum = new Float64Array(num_features);
+        const sum_sq = new Float64Array(num_features);
+
+        for (let i = 0; i < features_length; ++i) {
+            const offset = i * num_features;
+            for (let j = 0; j < num_features; ++j) {
+                const val = features_data[offset + j];
+                sum[j] += val;
+                sum_sq[j] += val * val;
+            }
+        }
+
+        // Skip normalization for empty/very short audio to avoid NaN from divide-by-zero.
+        if (features_length > 0) {
+            // Calculate mean and standard deviation, then normalize
+            const divisor = features_length > 1 ? features_length - 1 : 1;
+            for (let j = 0; j < num_features; ++j) {
+                const mean = sum[j] / features_length;
+                const variance = (sum_sq[j] - features_length * mean * mean) / divisor;
+                const std = Math.sqrt(Math.max(variance, 0)) + EPSILON;
+                const inv_std = 1 / std;
+
+                for (let i = 0; i < features_length; ++i) {
+                    const index = i * num_features + j;
+                    features_data[index] = (features_data[index] - mean) * inv_std;
+                }
+            }
+        }
+
+        const mask_data = new BigInt64Array(num_frames);
+        mask_data.fill(1n, 0, features_length);
+
+        let input_features = features.unsqueeze_(0);
+        const attention_mask = new Tensor('int64', mask_data, [1, num_frames]);
+
+        const result = {
+            input_features,
+            attention_mask,
+        };
+
+        if (this.delta_order > 0) {
+            const delta_result = computeTemporalDeltas(input_features, {
+                order: this.delta_order,
+                window: this.delta_window,
+                concatenate: this.delta_concatenate,
+            });
+            if (delta_result instanceof Tensor) {
+                input_features.dispose();
+                input_features = delta_result;
+                result.input_features = input_features;
+            } else {
+                result.delta_features = delta_result.delta;
+                if (delta_result.delta_delta) {
+                    result.delta_delta_features = delta_result.delta_delta;
+                }
+            }
+        }
+
+        return result;
+    }
+
+    clear_cache() {
+        this.feature_cache?.clear();
+    }
+
+    get_cache_stats() {
+        return this.feature_cache?.stats() ?? null;
+    }
+}