diff --git a/whisper/decoding.py b/whisper/decoding.py
index 49485d00..de86ce04 100644
--- a/whisper/decoding.py
+++ b/whisper/decoding.py
@@ -17,7 +17,7 @@
 
 @torch.no_grad()
 def detect_language(
-    model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None
+    model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None, language_bias: Optional[dict[str,float]] = None
 ) -> Tuple[Tensor, List[dict]]:
     """
     Detect the spoken language in the audio, and return them as list of strings, along with the ids
@@ -56,6 +56,14 @@ def detect_language(
     x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device)  # [n_audio, 1]
     logits = model.logits(x, mel)[:, 0]
 
+    # apply language_bias to logits
+    if language_bias:
+        biases = torch.zeros(logits.size(1), device=logits.device)
+        for lang, bias in language_bias.items():
+            token = tokenizer.to_language_token(lang)
+            biases[token] = bias
+        logits += biases
+
     # collect detected languages; suppress all non-language tokens
     mask = torch.ones(logits.shape[-1], dtype=torch.bool)
     mask[list(tokenizer.all_language_tokens)] = False
diff --git a/whisper/transcribe.py b/whisper/transcribe.py
index 1c075a20..5e7a946f 100644
--- a/whisper/transcribe.py
+++ b/whisper/transcribe.py
@@ -51,6 +51,7 @@ def transcribe(
     append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
     clip_timestamps: Union[str, List[float]] = "0",
     hallucination_silence_threshold: Optional[float] = None,
+    language_bias: Optional[dict[str,float]] = None,
     **decode_options,
 ):
     """
@@ -113,6 +114,10 @@ def transcribe(
         When word_timestamps is True, skip silent periods longer than this threshold (in seconds)
         when a possible hallucination is detected
 
+    language_bias: Optional[dict[str,float]] = None
+        A dictionary of language codes to positive or negative float values. These values will be
+        applied to the language detection logits before choosing the language.
+
     Returns
     -------
     A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
@@ -143,7 +148,7 @@ def transcribe(
                     "Detecting language using up to the first 30 seconds. Use `--language` to specify the language"
                 )
             mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
-            _, probs = model.detect_language(mel_segment)
+            _, probs = model.detect_language(mel_segment, language_bias=language_bias)
             decode_options["language"] = max(probs, key=probs.get)
             if verbose is not None:
                 print(