diff --git a/R/tokenizer.R b/R/tokenizer.R
index e74ca34..c454525 100644
--- a/R/tokenizer.R
+++ b/R/tokenizer.R
@@ -263,7 +263,16 @@ decode_bpe_bytes <- function(text) {
     }
   }
 
-  rawToChar(bytes)
+  # Write raw bytes to a connection and read back as UTF-8,
+
+  # replacing any invalid multibyte sequences
+  tmp <- tempfile()
+  on.exit(unlink(tmp), add = TRUE)
+  writeBin(bytes, tmp)
+  out <- readLines(tmp, warn = FALSE, encoding = "UTF-8")
+  out <- paste(out, collapse = "\n")
+  # Strip any remaining invalid bytes
+  iconv(out, from = "UTF-8", to = "UTF-8", sub = "")
 }
 
 #' Ensure Tokenizer Files are Downloaded
diff --git a/R/transcribe.R b/R/transcribe.R
index 431b182..5c0855e 100644
--- a/R/transcribe.R
+++ b/R/transcribe.R
@@ -271,68 +271,155 @@ transcribe_chunk <- function(
   dtype,
   verbose = TRUE
 ) {
-  # Convert audio to mel spectrogram
+  # Convert audio to mel spectrogram (full 30s window)
   if (verbose) message("Processing audio...")
-  mel <- audio_to_mel(file, n_mels = config$n_mels, device = device, dtype = dtype)
+  full_mel <- audio_to_mel(file, n_mels = config$n_mels, device = device, dtype = dtype)
+  n_frames <- full_mel$size(3)  # 3000 for 30s
 
   # Beam search needs timestamps internally for proper termination
-  # (without timestamps, the model generates repetitive tokens)
   user_timestamps <- timestamps
   internal_timestamps <- timestamps || beam_size > 1L
 
-  # Get initial decoder tokens (use model name for correct special token IDs)
-  initial_tokens <- get_initial_tokens(language, task,
-    model = config$model_name, timestamps = internal_timestamps)
-  tokens <- torch::torch_tensor(matrix(initial_tokens, nrow = 1),
-    dtype = torch::torch_long(),
-    device = device)
+  special <- whisper_special_tokens(config$model_name)
 
-  # Encode audio
-  if (verbose) message("Encoding audio...")
-  torch::with_no_grad({
+  # Seek loop: decode repeatedly, advancing through the mel spectrogram
+  seek <- 0L  # current frame position
+  all_generated <- integer(0)
+  all_cross_attn <- if (word_timestamps) list() else NULL
+  all_segments <- list()
+  seek_iter <- 0L
+
+  while (seek < n_frames) {
+    seek_iter <- seek_iter + 1L
+    if (seek_iter > 50L) break  # safety limit
+
+    # Slice mel from seek position, pad to full width
+    remaining <- n_frames - seek
+    if (remaining < 1L) break
+
+    if (seek == 0L) {
+      mel <- full_mel
+    } else {
+      # Slice mel[1, :, seek:] and pad to n_frames width
+      mel_slice <- full_mel[, , (seek + 1L):n_frames]
+      pad_width <- n_frames - mel_slice$size(3)
+      if (pad_width > 0L) {
+        mel <- torch::nnf_pad(mel_slice, c(0L, pad_width), value = 0)
+      } else {
+        mel <- mel_slice
+      }
+    }
+
+    # Compute seek time for this iteration
+    seek_time <- seek * 0.01  # frames to seconds (10ms per frame)
+
+    # Get initial decoder tokens
+    initial_tokens <- get_initial_tokens(language, task,
+      model = config$model_name, timestamps = internal_timestamps)
+    tokens <- torch::torch_tensor(matrix(initial_tokens, nrow = 1),
+      dtype = torch::torch_long(), device = device)
+
+    # Encode audio
+    torch::with_no_grad({
       encoder_output <- model$encode(mel)
     })
 
-  # Decode
-  if (verbose) message("Decoding...")
-  decode_result <- decode_with_fallback(model, encoder_output, tokens,
-    tokenizer, temperatures = temperatures, beam_size = beam_size,
-    best_of = best_of, max_length = config$n_text_ctx,
-    timestamps = internal_timestamps, word_timestamps = word_timestamps,
-    compression_ratio_threshold = compression_ratio_threshold,
-    logprob_threshold = logprob_threshold,
-    length_penalty = length_penalty, patience = patience,
-    device = device)
+    # Decode
+    decode_result <- decode_with_fallback(model, encoder_output, tokens,
+      tokenizer, temperatures = temperatures, beam_size = beam_size,
+      best_of = best_of, max_length = config$n_text_ctx,
+      timestamps = internal_timestamps, word_timestamps = word_timestamps,
+      compression_ratio_threshold = compression_ratio_threshold,
+      logprob_threshold = logprob_threshold,
+      length_penalty = length_penalty, patience = patience,
+      device = device)
+
+    generated <- decode_result$tokens
+
+    # Find the last timestamp token to determine where to seek next
+    last_ts_frame <- 0L
+    for (tok in generated) {
+      if (tok >= special$timestamp_begin) {
+        ts_seconds <- (tok - special$timestamp_begin) * 0.02
+        ts_frame <- as.integer(ts_seconds * 100)  # seconds to frames (10ms)
+        if (ts_frame > last_ts_frame) last_ts_frame <- ts_frame
+      }
+    }
+
+    # Extract segments with proper time offset
+    if (user_timestamps) {
+      segments <- extract_segments(generated, tokenizer,
+        time_offset = time_offset + seek_time)
+      if (nrow(segments) > 0) {
+        all_segments <- c(all_segments, list(segments))
+      }
+    }
+
+    # Collect cross-attention weights with seek offset
+    if (word_timestamps && !is.null(decode_result$cross_attn_weights)) {
+      all_cross_attn <- c(all_cross_attn, list(list(
+        weights = decode_result$cross_attn_weights,
+        tokens = generated,
+        initial_tokens = initial_tokens,
+        seek_time = seek_time
+      )))
+    }
+
+    # Advance seek position
+    if (last_ts_frame > 0L) {
+      seek <- seek + last_ts_frame
+    } else {
+      # No timestamp found — model produced no timed output, skip ahead
+      break
+    }
+
+    # If last timestamp covered nearly the full remaining audio, stop
+    if (last_ts_frame >= remaining - 100L) break
+  }
 
-  generated <- decode_result$tokens
-  cross_attn_weights <- decode_result$cross_attn_weights
+  if (verbose && seek_iter > 1L) {
+    message("  Seek loop: ", seek_iter, " iterations")
+  }
 
   # Build result
   if (user_timestamps) {
-    # Extract segments from timestamp tokens
-    segments <- extract_segments(generated, tokenizer, time_offset = time_offset)
-    text <- paste(segments$text, collapse = " ")
-    text <- clean_text(text)
+    if (length(all_segments) > 0) {
+      segments <- do.call(rbind, all_segments)
+      text <- paste(segments$text, collapse = " ")
+      text <- clean_text(text)
+    } else {
+      segments <- data.frame(start = numeric(0), end = numeric(0),
+        text = character(0))
+      text <- ""
+    }
     result <- list(text = text, language = language, segments = segments)
   } else {
-    # Strip timestamp tokens if used internally for beam search
+    # For non-timestamp mode, combine all generated tokens
+    # (strip timestamp tokens if used internally)
     if (internal_timestamps) {
-      special <- whisper_special_tokens(config$model_name)
-      generated <- generated[generated < special$timestamp_begin]
+      all_generated <- all_generated[all_generated < special$timestamp_begin]
     }
-    text <- tokenizer$decode(generated)
+    text <- tokenizer$decode(all_generated)
     text <- clean_text(text)
     result <- list(text = text, language = language)
   }
 
-  # Word-level timestamps via cross-attention DTW
-  if (word_timestamps && !is.null(cross_attn_weights)) {
-    special <- whisper_special_tokens(config$model_name)
-    sample_begin <- length(initial_tokens)
-    words <- compute_word_timestamps(generated, cross_attn_weights,
-      tokenizer, config, time_offset = time_offset,
-      sample_begin = sample_begin)
-    result$words <- words
+  # Word-level timestamps via cross-attention DTW (per seek iteration)
+  if (word_timestamps && length(all_cross_attn) > 0) {
+    all_words <- list()
+    for (ca in all_cross_attn) {
+      sample_begin <- length(ca$initial_tokens)
+      words <- compute_word_timestamps(ca$tokens, ca$weights,
+        tokenizer, config,
+        time_offset = time_offset + ca$seek_time,
+        sample_begin = sample_begin)
+      if (!is.null(words) && nrow(words) > 0) {
+        all_words <- c(all_words, list(words))
+      }
+    }
+    if (length(all_words) > 0) {
+      result$words <- do.call(rbind, all_words)
+    }
   }
 
   result
@@ -636,6 +723,9 @@ transcribe_long <- function(
     }
   }
 
+  # Get actual audio duration to filter hallucinations from padded chunks
+  audio_dur <- audio_duration(file)
+
   # Combine results
   result <- list(
     text = paste(all_text, collapse = " "),
@@ -643,18 +733,73 @@ transcribe_long <- function(
   )
 
   if (timestamps) {
-    result$segments <- if (length(all_segments) > 0) {
-      do.call(rbind, all_segments)
+    if (length(all_segments) > 0) {
+      combined <- do.call(rbind, all_segments)
+      # Remove segments that start after the actual audio duration
+      combined <- combined[combined$start < audio_dur, , drop = FALSE]
+      # Cap end times to audio duration
+      combined$end <- pmin(combined$end, audio_dur)
+      # Deduplicate overlapping segments at chunk boundaries.
+      # Strategy: when two segments overlap, keep the later one (from the
+      # chunk that has actual audio for that time region) unless it looks
+      # hallucinated (very short text).
+      if (nrow(combined) > 1) {
+        keep <- rep(TRUE, nrow(combined))
+        for (j in 2:nrow(combined)) {
+          if (combined$start[j] < combined$end[j - 1] - 0.1) {
+            # Overlap detected
+            prev_len <- nchar(combined$text[j - 1])
+            curr_len <- nchar(combined$text[j])
+            if (curr_len < 5) {
+              # Later segment is likely hallucination, drop it
+              keep[j] <- FALSE
+            } else if (prev_len < 5) {
+              # Previous segment is likely hallucination, drop it
+              keep[j - 1] <- FALSE
+            } else {
+              # Both substantial: trim previous to end before overlap starts
+              combined$end[j - 1] <- combined$start[j]
+            }
+          }
+        }
+        combined <- combined[keep, , drop = FALSE]
+      }
+      # Remove likely hallucinated segments (very short duration + short text)
+      seg_dur <- combined$end - combined$start
+      combined <- combined[!(seg_dur < 0.5 & nchar(combined$text) < 15), , drop = FALSE]
+      result$segments <- combined
     } else {
-      data.frame(start = numeric(0), end = numeric(0), text = character(0))
+      result$segments <- data.frame(start = numeric(0), end = numeric(0),
+        text = character(0))
     }
   }
 
   if (word_timestamps) {
-    result$words <- if (length(all_words) > 0) {
-      do.call(rbind, all_words)
+    if (length(all_words) > 0) {
+      combined_words <- do.call(rbind, all_words)
+      # Remove words that start after actual audio duration
+      combined_words <- combined_words[combined_words$start < audio_dur, , drop = FALSE]
+      # Cap word end times
+      combined_words$end <- pmin(combined_words$end, audio_dur)
+      # Remove duplicate words from chunk overlap (keep first occurrence)
+      if (nrow(combined_words) > 1) {
+        keep <- rep(TRUE, nrow(combined_words))
+        for (j in 2:nrow(combined_words)) {
+          if (combined_words$start[j] < combined_words$end[j - 1] - 0.05) {
+            keep[j] <- FALSE
+          }
+        }
+        combined_words <- combined_words[keep, , drop = FALSE]
+      }
+      # Filter words to only those within retained segments
+      if (!is.null(result$segments) && nrow(result$segments) > 0) {
+        seg_end <- max(result$segments$end)
+        combined_words <- combined_words[combined_words$start < seg_end, , drop = FALSE]
+      }
+      result$words <- combined_words
     } else {
-      data.frame(word = character(0), start = numeric(0), end = numeric(0))
+      result$words <- data.frame(word = character(0), start = numeric(0),
+        end = numeric(0))
     }
   }